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| 2025 | A Simple Model of Inference Scaling Laws. Noam Itzhak Levi |
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| 2025 | A Trichotomy for List Transductive Online Learning. Steve Hanneke, Amirreza Shaeiri |
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| 2025 | A Unified Comparative Study with Generalized Conformity Scores for Multi-Output Conformal Regression. Victor Dheur, Matteo Fontana, Yorick Estievenart, Naomi Desobry, Souhaib Ben Taieb |
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| 2025 | A Variational Information Theoretic Approach to Out-of-Distribution Detection. Sudeepta Mondal, Zhuolin Jiang, Ganesh Sundaramoorthi |
| 2025 | A Variational Perspective on Generative Protein Fitness Optimization. Lea Bogensperger, Dominik Narnhofer, Ahmed Allam, Konrad Schindler, Michael Krauthammer |
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| 2025 | A-PSRO: A Unified Strategy Learning Method with Advantage Metric for Normal-form Games. Yudong Hu, Haoran Li, Congying Han, Tiande Guo, Bonan Li, Mingqiang Li |
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| 2025 | ABNet: Adaptive explicit-Barrier Net for Safe and Scalable Robot Learning. Wei Xiao, Tsun-Hsuan Wang, Chuang Gan, Daniela Rus |
| 2025 | ADDQ: Adaptive distributional double Q-learning. Leif Döring, Benedikt Wille, Maximilian Birr, Mihail Bîrsan, Martin Slowik |
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| 2025 | AEQA-NAT : Adaptive End-to-end Quantization Alignment Training Framework for Non-autoregressive Machine Translation. Xiangyu Qu, Guojing Liu, Liang Li |
| 2025 | AGAV-Rater: Adapting Large Multimodal Model for AI-Generated Audio-Visual Quality Assessment. Yuqin Cao, Xiongkuo Min, Yixuan Gao, Wei Sun, Guangtao Zhai |
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| 2025 | AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings. Yilin Ye, Junchao Huang, Xingchen Zeng, Jiazhi Xia, Wei Zeng |
| 2025 | ALMTokenizer: A Low-bitrate and Semantic-rich Audio Codec Tokenizer for Audio Language Modeling. Dongchao Yang, Songxiang Liu, Haohan Guo, Jiankun Zhao, Yuanyuan Wang, Helin Wang, Zeqian Ju, Xubo Liu, Xueyuan Chen, Xu Tan, Xixin Wu, Helen M. Meng |
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| 2025 | ARS: Adaptive Reward Scaling for Multi-Task Reinforcement Learning. Myungsik Cho, Jongeui Park, Jeonghye Kim, Youngchul Sung |
| 2025 | ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning. Arto Maranjyan, El Mehdi Saad, Peter Richtárik, Francesco Orabona |
| 2025 | AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation. Prashanth Vijayaraghavan, Luyao Shi, Ehsan Degan, Vandana V. Mukherjee, Xin Zhang |
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| 2025 | Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators. Ze Cheng, Zhuoyu Li, Xiaoqiang Wang, Jianing Huang, Zhizhou Zhang, Zhongkai Hao, Hang Su |
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| 2025 | Action-Constrained Imitation Learning. Chia-Han Yeh, Tse-Sheng Nan, Risto Vuorio, Wei Hung, Hung-Yen Wu, Shao-Hua Sun, Ping-Chun Hsieh |
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| 2025 | Active Fine-Tuning of Multi-Task Policies. Marco Bagatella, Jonas Hübotter, Georg Martius, Andreas Krause |
| 2025 | Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations. Jason Qin, Hans-Hermann Wessels, Carlos Fernandez-Granda, Yuhan Hao |
| 2025 | Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes. Erica Zhang, Fangzhao Zhang, Mert Pilanci |
| 2025 | Active Learning with Selective Time-Step Acquisition for PDEs. Yegon Kim, Hyunsu Kim, Gyeonghoon Ko, Juho Lee |
| 2025 | Active Reward Modeling: Adaptive Preference Labeling for Large Language Model Alignment. Yunyi Shen, Hao Sun, Jean-Francois Ton |
| 2025 | Active Treatment Effect Estimation via Limited Samples. Zhiheng Zhang, Haoxiang Wang, Haoxuan Li, Zhouchen Lin |
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| 2025 | Actor-Critics Can Achieve Optimal Sample Efficiency. Kevin Tan, Wei Fan, Yuting Wei |
| 2025 | Ad Hoc Teamwork via Offline Goal-Based Decision Transformers. Xinzhi Zhang, Hohei Chan, Deheng Ye, Yi Cai, Mengchen Zhao |
| 2025 | Ad-Hoc Human-AI Coordination Challenge. Tin Dizdarevic, Ravi Hammond, Tobias Gessler, Anisoara Calinescu, Jonathan Cook, Matteo Gallici, Andrei Lupu, Jakob Nicolaus Foerster |
| 2025 | AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism. Zhepei Wei, Wei-Lin Chen, Xinyu Zhu, Yu Meng |
| 2025 | AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting. Abdelhakim Benechehab, Vasilii Feofanov, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl |
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| 2025 | AdaWorld: Learning Adaptable World Models with Latent Actions. Shenyuan Gao, Siyuan Zhou, Yilun Du, Jun Zhang, Chuang Gan |
| 2025 | Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation. Jintao Tong, Ran Ma, Yixiong Zou, Guangyao Chen, Yuhua Li, Ruixuan Li |
| 2025 | Adapting Precomputed Features for Efficient Graph Condensation. Yuan Li, Jun Hu, Zemin Liu, Bryan Hooi, Jia Chen, Bingsheng He |
| 2025 | Adapting While Learning: Grounding LLMs for Scientific Problems with Tool Usage Adaptation. Bohan Lyu, Yadi Cao, Duncan Watson-Parris, Leon Bergen, Taylor Berg-Kirkpatrick, Rose Yu |
| 2025 | Adapting to Evolving Adversaries with Regularized Continual Robust Training. Sihui Dai, Christian Cianfarani, Vikash Sehwag, Prateek Mittal, Arjun Nitin Bhagoji |
| 2025 | Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning. Erchi Wang, Yuqing Zhu, Yu-Xiang Wang |
| 2025 | Adaptive Data Collection for Robust Learning Across Multiple Distributions. Chengbo Zang, Mehmet Kerem Türkcan, Gil Zussman, Zoran Kostic, Javad Ghaderi |
| 2025 | Adaptive Elicitation of Latent Information Using Natural Language. Jimmy Wang, Thomas P. Zollo, Richard S. Zemel, Hongseok Namkoong |
| 2025 | Adaptive Estimation and Learning under Temporal Distribution Shift. Dheeraj Baby, Yifei Tang, Hieu Duy Nguyen, Yu-Xiang Wang, Rohit Pyati |
| 2025 | Adaptive Exploration for Multi-Reward Multi-Policy Evaluation. Alessio Russo, Aldo Pacchiano |
| 2025 | Adaptive Flow Matching for Resolving Small-Scale Physics. Stathi Fotiadis, Noah D. Brenowitz, Tomas Geffner, Yair Cohen, Michael S. Pritchard, Arash Vahdat, Morteza Mardani |
| 2025 | Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection. Matteo Zecchin, Sangwoo Park, Osvaldo Simeone |
| 2025 | Adaptive Localization of Knowledge Negation for Continual LLM Unlearning. Abudukelimu Wuerkaixi, Qizhou Wang, Sen Cui, Wutong Xu, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang |
| 2025 | Adaptive Median Smoothing: Adversarial Defense for Unlearned Text-to-Image Diffusion Models at Inference Time. Xiaoxuan Han, Songlin Yang, Wei Wang, Yang Li, Jing Dong |
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| 2025 | Adaptive Partitioning Schemes for Optimistic Optimization. Raja Sunkara, Ardhendu Tripathy |
| 2025 | Adaptive Sample Sharing for Multi Agent Linear Bandits. Hamza Cherkaoui, Merwan Barlier, Igor Colin |
| 2025 | Adaptive Self-improvement LLM Agentic System for ML Library Development. Genghan Zhang, Weixin Liang, Olivia Hsu, Kunle Olukotun |
| 2025 | Adaptive Sensitivity Analysis for Robust Augmentation against Natural Corruptions in Image Segmentation. Laura Yu Zheng, Wenjie Wei, Tony Wu, Jacob Clements, Shreelekha Revankar, Andre Harrison, Yu Shen, Ming C. Lin |
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| 2025 | AdaptiveStep: Automatically Dividing Reasoning Step through Model Confidence. Yuliang Liu, Junjie Lu, Chaofeng Qu, Zhaoling Chen, Zefan Cai, Jason Klein Liu, Chonghan Liu, Yunhui Xia, Li Zhao, Jiang Bian, Chuheng Zhang, Wei Shen, Zhouhan Lin |
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| 2025 | Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts. Lan Li, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan |
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| 2025 | Adjustment for Confounding using Pre-Trained Representations. Rickmer Schulte, David Rügamer, Thomas Nagler |
| 2025 | AdvAgent: Controllable Blackbox Red-teaming on Web Agents. Chejian Xu, Mintong Kang, Jiawei Zhang, Zeyi Liao, Lingbo Mo, Mengqi Yuan, Huan Sun, Bo Li |
| 2025 | AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion Models. Yaopei Zeng, Yuanpu Cao, Bochuan Cao, Yurui Chang, Jinghui Chen, Lu Lin |
| 2025 | AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs. Anselm Paulus, Arman Zharmagambetov, Chuan Guo, Brandon Amos, Yuandong Tian |
| 2025 | Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations. Davide Sartor, Alberto Sinigaglia, Gian Antonio Susto |
| 2025 | Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge. Hanglei Hu, Yingying Guo, Zhikang Chen, Sen Cui, Fei Wu, Kun Kuang, Min Zhang, Bo Jiang |
| 2025 | Adversarial Combinatorial Semi-bandits with Graph Feedback. Yuxiao Wen |
| 2025 | Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets. Wei Liu, Zhongyu Niu, Lang Gao, Zhiying Deng, Jun Wang, Haozhao Wang, Ruixuan Li |
| 2025 | Adversarial Inception Backdoor Attacks against Reinforcement Learning. Ethan Rathbun, Alina Oprea, Christopher Amato |
| 2025 | Adversarial Inputs for Linear Algebra Backends. Jonas Möller, Lukas Pirch, Felix Weissberg, Sebastian Baunsgaard, Thorsten Eisenhofer, Konrad Rieck |
| 2025 | Adversarial Perturbations Are Formed by Iteratively Learning Linear Combinations of the Right Singular Vectors of the Adversarial Jacobian. Thomas Paniagua, Chinmay Savadikar, Tianfu Wu |
| 2025 | Adversarial Reasoning at Jailbreaking Time. Mahdi Sabbaghi, Paul Kassianik, George J. Pappas, Amin Karbasi, Hamed Hassani |
| 2025 | Adversarial Robust Generalization of Graph Neural Networks. Chang Cao, Han Li, Yulong Wang, Rui Wu, Hong Chen |
| 2025 | Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees. Yannis Montreuil, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi |
| 2025 | Adversarial Robustness via Deformable Convolution with Stochasticity. Yanxiang Ma, Zixuan Huang, Minjing Dong, Shan You, Chang Xu |
| 2025 | Adversaries Can Misuse Combinations of Safe Models. Erik Jones, Anca D. Dragan, Jacob Steinhardt |
| 2025 | Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks. Nurbek Tastan, Samuel Horváth, Karthik Nandakumar |
| 2025 | AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models. Zheng Lian, Haoyu Chen, Lan Chen, Haiyang Sun, Licai Sun, Yong Ren, Zebang Cheng, Bin Liu, Rui Liu, Xiaojiang Peng, Jiangyan Yi, Jianhua Tao |
| 2025 | AffinityFlow: Guided Flows for Antibody Affinity Maturation. Can Chen, Karla-Luise Herpoldt, Chenchao Zhao, Zichen Wang, Marcus D. Collins, Shang Shang, Ron Benson |
| 2025 | Agent Reviewers: Domain-specific Multimodal Agents with Shared Memory for Paper Review. Kai Lu, Shixiong Xu, Jinqiu Li, Kun Ding, Gaofeng Meng |
| 2025 | Agent Workflow Memory. Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, Graham Neubig |
| 2025 | Agent-Centric Actor-Critic for Asynchronous Multi-Agent Reinforcement Learning. Whiyoung Jung, Sunghoon Hong, Deunsol Yoon, Kanghoon Lee, Woohyung Lim |
| 2025 | Agent-as-a-Judge: Evaluate Agents with Agents. Mingchen Zhuge, Changsheng Zhao, Dylan R. Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chandra, Jürgen Schmidhuber |
| 2025 | Aggregation Buffer: Revisiting DropEdge with a New Parameter Block. Dooho Lee, Myeong Kong, Sagad Hamid, Cheonwoo Lee, Jaemin Yoo |
| 2025 | Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders. Rogelio Andrade Mancisidor, Robert Jenssen, Shujian Yu, Michael Kampffmeyer |
| 2025 | Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction. Yiheng Xu, Zekun Wang, Junli Wang, Dunjie Lu, Tianbao Xie, Amrita Saha, Doyen Sahoo, Tao Yu, Caiming Xiong |
| 2025 | Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery. Pratinav Seth, Michelle P. Lin, Brefo Yaw Dwamena, Jade Boutot, Mary Kang, David Rolnick |
| 2025 | Algorithm Development in Neural Networks: Insights from the Streaming Parity Task. Loek van Rossem, Andrew M. Saxe |
| 2025 | Algorithmic Recourse for Long-Term Improvement. Kentaro Kanamori, Ken Kobayashi, Satoshi Hara, Takuya Takagi |
| 2025 | Algorithms and Hardness for Active Learning on Graphs. Vincent Cohen-Addad, Silvio Lattanzi, Simon Meierhans |
| 2025 | Algorithms with Calibrated Machine Learning Predictions. Judy Hanwen Shen, Ellen Vitercik, Anders Wikum |
| 2025 | Aligned Multi Objective Optimization. Yonathan Efroni, Ben Kretzu, Daniel Jiang, Jalaj Bhandari, Zheqing Zhu, Karen Ullrich |
| 2025 | Aligning LLMs by Predicting Preferences from User Writing Samples. Stéphane Aroca-Ouellette, Natalie Mackraz, Barry-John Theobald, Katherine Metcalf |
| 2025 | Aligning Multimodal Representations through an Information Bottleneck. Antonio Almudévar, José Miguel Hernández-Lobato, Sameer Khurana, Ricard Marxer, Alfonso Ortega |
| 2025 | Aligning Protein Conformation Ensemble Generation with Physical Feedback. Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang |
| 2025 | Aligning Spoken Dialogue Models from User Interactions. Anne Wu, Laurent Mazaré, Neil Zeghidour, Alexandre Défossez |
| 2025 | Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models. Yinhong Liu, Zhijiang Guo, Tianya Liang, Ehsan Shareghi, Ivan Vulic, Nigel Collier |
| 2025 | All-Purpose Mean Estimation over R: Optimal Sub-Gaussianity with Outlier Robustness and Low Moments Performance. Jasper C. H. Lee, Walter McKelvie, Maoyuan Song, Paul Valiant |
| 2025 | All-atom Diffusion Transformers: Unified generative modelling of molecules and materials. Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, Zachary W. Ulissi |
| 2025 | All-atom inverse protein folding through discrete flow matching. Kai Yi, Kiarash Jamali, Sjors H. W. Scheres |
| 2025 | Almost Optimal Fully Dynamic k-Center Clustering with Recourse. Sayan Bhattacharya, Martín Costa, Ermiya Farokhnejad, Silvio Lattanzi, Nikos Parotsidis |
| 2025 | Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search. Boyan Li, Jiayi Zhang, Ju Fan, Yanwei Xu, Chong Chen, Nan Tang, Yuyu Luo |
| 2025 | AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization. Junkang Wu, Xue Wang, Zhengyi Yang, Jiancan Wu, Jinyang Gao, Bolin Ding, Xiang Wang, Xiangnan He |
| 2025 | AlphaPO: Reward Shape Matters for LLM Alignment. Aman Gupta, Shao Tang, Qingquan Song, Sirou Zhu, Jiwoo Hong, Ankan Saha, Viral Gupta, Noah Lee, Eunki Kim, Siyu Zhu, Parag Agrawal, Natesh S. Pillai, S. Sathiya Keerthi |
| 2025 | AlphaQCM: Alpha Discovery in Finance with Distributional Reinforcement Learning. Zhoufan Zhu, Ke Zhu |
| 2025 | AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement. Pranjal Aggarwal, Bryan Parno, Sean Welleck |
| 2025 | An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures. Thibaut Boissin, Franck Mamalet, Thomas Fel, Agustin Martin Picard, Thomas Massena, Mathieu Serrurier |
| 2025 | An All-Atom Generative Model for Designing Protein Complexes. Ruizhe Chen, Dongyu Xue, Xiangxin Zhou, Zaixiang Zheng, Xiangxiang Zeng, Quanquan Gu |
| 2025 | An Analysis for Reasoning Bias of Language Models with Small Initialization. Junjie Yao, Zhongwang Zhang, Zhi-Qin John Xu |
| 2025 | An Architecture Search Framework for Inference-Time Techniques. Jon Saad-Falcon, Adrian Gamarra Lafuente, Shlok Natarajan, Nahum Maru, Hristo Todorov, Etash Kumar Guha, Estefany Kelly Buchanan, Mayee F. Chen, Neel Guha, Christopher Ré, Azalia Mirhoseini |
| 2025 | An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale. Fabian Christian Spaeh, Atsushi Miyauchi |
| 2025 | An Augmentation-Aware Theory for Self-Supervised Contrastive Learning. Jingyi Cui, Hongwei Wen, Yisen Wang |
| 2025 | An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective. Xinyue Chen, Jinfeng Peng, Yuhao Li, Xiaorong Pu, Yang Yang, Yazhou Ren |
| 2025 | An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks. Mohsen Dehghankar, Mahdi Erfanian, Abolfazl Asudeh |
| 2025 | An Efficient Private GPT Never Autoregressively Decodes. Zhengyi Li, Yue Guan, Kang Yang, Yu Feng, Ning Liu, Yu Yu, Jingwen Leng, Minyi Guo |
| 2025 | An Efficient Pruner for Large Language Model with Theoretical Guarantee. Canhong Wen, Yihong Zuo, Wenliang Pan |
| 2025 | An Efficient Search-and-Score Algorithm for Ancestral Graphs using Multivariate Information Scores for Complex Non-linear and Categorical Data. Nikita Lagrange, Hervé Isambert |
| 2025 | An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability. Daiqing Wu, Dongbao Yang, Sicheng Zhao, Can Ma, Yu Zhou |
| 2025 | An End-to-End Model for Logits-Based Large Language Models Watermarking. Kahim Wong, Jicheng Zhou, Jiantao Zhou, Yain-Whar Si |
| 2025 | An Error Analysis of Flow Matching for Deep Generative Modeling. Zhengyu Zhou, Weiwei Liu |
| 2025 | An Expressive and Self-Adaptive Dynamical System for Efficient Function Learning. Chuan Liu, Chunshu Wu, Ruibing Song, Ang Li, Ying Nian Wu, Tong Geng |
| 2025 | An Improved Clique-Picking Algorithm for Counting Markov Equivalent DAGs via Super Cliques Transfer. Lifu Liu, Shiyuan He, Jianhua Guo |
| 2025 | An Instrumental Value for Data Production and its Application to Data Pricing. Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Haifeng Xu |
| 2025 | An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks. Valentyn Boreiko, Alexander Panfilov, Václav Vorácek, Matthias Hein, Jonas Geiping |
| 2025 | An Online Adaptive Sampling Algorithm for Stochastic Difference-of-convex Optimization with Time-varying Distributions. Yuhan Ye, Ying Cui, Jingyi Wang |
| 2025 | An Online Statistical Framework for Out-of-Distribution Detection. Xinsong Ma, Xin Zou, Weiwei Liu |
| 2025 | An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints. Jiahui Zhu, Kihyun Yu, Dabeen Lee, Xin Liu, Honghao Wei |
| 2025 | An analytic theory of creativity in convolutional diffusion models. Mason Kamb, Surya Ganguli |
| 2025 | An in depth look at the Procrustes-Wasserstein distance: properties and barycenters. Davide Adamo, Marco Corneli, Manon Vuillien, Emmanuelle Vila |
| 2025 | AnalogGenie-Lite: Enhancing Scalability and Precision in Circuit Topology Discovery through Lightweight Graph Modeling. Jian Gao, Weidong Cao, Xuan Zhang |
| 2025 | Analytical Construction on Geometric Architectures: Transitioning from Static to Temporal Link Prediction. Yadong Sun, Xiaofeng Cao, Ivor W. Tsang, Heng Tao Shen |
| 2025 | Analytical Lyapunov Function Discovery: An RL-based Generative Approach. Haohan Zou, Jie Feng, Hao Zhao, Yuanyuan Shi |
| 2025 | Analyze Feature Flow to Enhance Interpretation and Steering in Language Models. Daniil Laptev, Nikita Balagansky, Yaroslav Aksenov, Daniil Gavrilov |
| 2025 | Angle Domain Guidance: Latent Diffusion Requires Rotation Rather Than Extrapolation. Cheng Jin, Zhenyu Xiao, Chutao Liu, Yuantao Gu |
| 2025 | Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions. Dongze Wu, Yao Xie |
| 2025 | Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning Attack. Tiansheng Huang, Gautam Bhattacharya, Pratik Joshi, Joshua Kimball, Ling Liu |
| 2025 | AnyEdit: Edit Any Knowledge Encoded in Language Models. Houcheng Jiang, Junfeng Fang, Ningyu Zhang, Mingyang Wan, Guojun Ma, Xiang Wang, Xiangnan He, Tat-Seng Chua |
| 2025 | Anytime-Constrained Equilibria in Polynomial Time. Jeremy McMahan |
| 2025 | Approximate Differential Privacy of the ℓ2 Mechanism. Matthew Joseph, Alex Kulesza, Alexander Yu |
| 2025 | Approximate Forest Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees. Nate Veldt, Thomas Stanley, Benjamin W. Priest, Trevor Steil, Keita Iwabuchi, T. S. Jayram, Geoffrey Sanders |
| 2025 | Approximately Correct Label Distribution Learning. Weiwei Li, Haitao Wu, Yunan Lu, Xiuyi Jia |
| 2025 | Approximating Latent Manifolds in Neural Networks via Vanishing Ideals. Nico Pelleriti, Max Zimmer, Elias Samuel Wirth, Sebastian Pokutta |
| 2025 | Approximation to Smooth Functions by Low-Rank Swish Networks. Zimeng Li, Hongjun Li, Jingyuan Wang, Ke Tang |
| 2025 | Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation. Da Long, Zhitong Xu, Guang Yang, Akil Narayan, Shandian Zhe |
| 2025 | Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models. Thomas Fel, Ekdeep Singh Lubana, Jacob S. Prince, Matthew Kowal, Victor Boutin, Isabel Papadimitriou, Binxu Wang, Martin Wattenberg, Demba E. Ba, Talia Konkle |
| 2025 | Are High-Quality AI-Generated Images More Difficult for Models to Detect? Yao Xiao, Binbin Yang, Weiyan Chen, Jiahao Chen, Zijie Cao, Ziyi Dong, Xiangyang Ji, Liang Lin, Wei Ke, Pengxu Wei |
| 2025 | Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle. Hui Dai, Ryan Teehan, Mengye Ren |
| 2025 | Are Large Brainwave Foundation Models Capable Yet ? Insights from Fine-Tuning. Na Lee, Konstantinos Barmpas, Yannis Panagakis, Dimitrios A. Adamos, Nikolaos A. Laskaris, Stefanos Zafeiriou |
| 2025 | Are Large Language Models Ready for Multi-Turn Tabular Data Analysis? Jinyang Li, Nan Huo, Yan Gao, Jiayi Shi, Yingxiu Zhao, Ge Qu, Bowen Qin, Yurong Wu, Xiaodong Li, Chenhao Ma, Jian-Guang Lou, Reynold Cheng |
| 2025 | Are Sparse Autoencoders Useful? A Case Study in Sparse Probing. Subhash Kantamneni, Joshua Engels, Senthooran Rajamanoharan, Max Tegmark, Neel Nanda |
| 2025 | Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster. Sharan Vaswani, Reza Babanezhad Harikandeh |
| 2025 | ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior. Zhongweiyang Xu, Xulin Fan, Zhong-Qiu Wang, Xilin Jiang, Romit Roy Choudhury |
| 2025 | Arrow: Accelerator for Time Series Causal Discovery with Time Weaving. Yuanyuan Yao, Yuan Dong, Lu Chen, Kun Kuang, Ziquan Fang, Cheng Long, Yunjun Gao, Tianyi Li |
| 2025 | Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models. Kejia Chen, Jiawen Zhang, Jiacong Hu, Yu Wang, Jian Lou, Zunlei Feng, Mingli Song |
| 2025 | AssistanceZero: Scalably Solving Assistance Games. Cassidy Laidlaw, Eli Bronstein, Timothy Guo, Dylan Feng, Lukas Berglund, Justin Svegliato, Stuart Russell, Anca D. Dragan |
| 2025 | AsymRnR: Video Diffusion Transformers Acceleration with Asymmetric Reduction and Restoration. Wenhao Sun, Rong-Cheng Tu, Jingyi Liao, Zhao Jin, Dacheng Tao |
| 2025 | Asymmetric Decision-Making in Online Knowledge Distillation: Unifying Consensus and Divergence. Zhaowei Chen, Borui Zhao, Yuchen Ge, Yuhao Chen, Renjie Song, Jiajun Liang |
| 2025 | AtlasD: Automatic Local Symmetry Discovery. Manu Bhat, JongHyun Park, Jianke Yang, Nima Dehmamy, Robin Walters, Rose Yu |
| 2025 | Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data. Zhong Guan, Likang Wu, Hongke Zhao, Ming He, Jianping Fan |
| 2025 | Attention-Level Speculation. Jack Cai, Ammar Vora, Randolph Zhang, Mark O'Connor, Mark C. Jeffrey |
| 2025 | Attention-Only Transformers via Unrolled Subspace Denoising. Peng Wang, Yifu Lu, Yaodong Yu, Druv Pai, Qing Qu, Yi Ma |
| 2025 | Attributes Shape the Embedding Space of Face Recognition Models. Pierrick Leroy, Antonio Mastropietro, Marco Nurisso, Francesco Vaccarino |
| 2025 | AuPair: Golden Example Pairs for Code Repair. Aditi Mavalankar, Hassan Mansoor, Zita Marinho, Mariia Samsikova, Tom Schaul |
| 2025 | Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities. Sreyan Ghosh, Zhifeng Kong, Sonal Kumar, S. Sakshi, Jaehyeon Kim, Wei Ping, Rafael Valle, Dinesh Manocha, Bryan Catanzaro |
| 2025 | Auditing $f$-differential privacy in one run. Saeed Mahloujifar, Luca Melis, Kamalika Chaudhuri |
| 2025 | Auditing Prompt Caching in Language Model APIs. Chenchen Gu, Xiang Lisa Li, Rohith Kuditipudi, Percy Liang, Tatsunori Hashimoto |
| 2025 | Auto-reconfiguration for Latency Minimization in CPU-based DNN Serving. Ankit Bhardwaj, Amar Phanishayee, Deepak Narayanan, Ryan Stutsman |
| 2025 | AutoAL: Automated Active Learning with Differentiable Query Strategy Search. Yifeng Wang, Xueying Zhan, Siyu Huang |
| 2025 | AutoAdvExBench: Benchmarking Autonomous Exploitation of Adversarial Example Defenses. Nicholas Carlini, Edoardo Debenedetti, Javier Rando, Milad Nasr, Florian Tramèr |
| 2025 | AutoCATE: End-to-End, Automated Treatment Effect Estimation. Toon Vanderschueren, Tim Verdonck, Mihaela van der Schaar, Wouter Verbeke |
| 2025 | AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling. Alexander Capstick, Rahul G. Krishnan, Payam M. Barnaghi |
| 2025 | AutoEval Done Right: Using Synthetic Data for Model Evaluation. Pierre Boyeau, Anastasios Nikolas Angelopoulos, Tianle Li, Nir Yosef, Jitendra Malik, Michael I. Jordan |
| 2025 | AutoGFM: Automated Graph Foundation Model with Adaptive Architecture Customization. Haibo Chen, Xin Wang, Zeyang Zhang, Haoyang Li, Ling Feng, Wenwu Zhu |
| 2025 | AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML. Patara Trirat, Wonyong Jeong, Sung Ju Hwang |
| 2025 | AutoStep: Locally adaptive involutive MCMC. Tiange Liu, Nikola Surjanovic, Miguel Biron-Lattes, Alexandre Bouchard-Côté, Trevor Campbell |
| 2025 | Autoencoder-Based Hybrid Replay for Class-Incremental Learning. Milad Khademi Nori, Il-Min Kim, Guanghui Wang |
| 2025 | Autoformulation of Mathematical Optimization Models Using LLMs. Nicolás Astorga, Tennison Liu, Yuanzhang Xiao, Mihaela van der Schaar |
| 2025 | Automated Benchmark Generation for Repository-Level Coding Tasks. Konstantinos Vergopoulos, Mark Niklas Müller, Martin T. Vechev |
| 2025 | Automated Hypothesis Validation with Agentic Sequential Falsifications. Kexin Huang, Ying Jin, Ryan Li, Michael Y. Li, Emmanuel J. Candès, Jure Leskovec |
| 2025 | Automated Red Teaming with GOAT: the Generative Offensive Agent Tester. Maya Pavlova, Erik Brinkman, Krithika Iyer, Vítor Albiero, Joanna Bitton, Hailey Nguyen, Cristian Canton Ferrer, Ivan Evtimov, Aaron Grattafiori |
| 2025 | Automatic Differentiation of Optimization Algorithms with Time-Varying Updates. Sheheryar Mehmood, Peter Ochs |
| 2025 | Automatic Reward Shaping from Confounded Offline Data. Mingxuan Li, Junzhe Zhang, Elias Bareinboim |
| 2025 | Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios. Xihong Yang, Siwei Wang, Fangdi Wang, Jiaqi Jin, Suyuan Liu, Yue Liu, En Zhu, Xinwang Liu, Yueming Jin |
| 2025 | Automatically Interpreting Millions of Features in Large Language Models. Gonçalo Paulo, Alex Mallen, Caden Juang, Nora Belrose |
| 2025 | Autonomy-of-Experts Models. Ang Lv, Ruobing Xie, Yining Qian, Songhao Wu, Xingwu Sun, Zhanhui Kang, Di Wang, Rui Yan |
| 2025 | Average Certified Radius is a Poor Metric for Randomized Smoothing. Chenhao Sun, Yuhao Mao, Mark Niklas Müller, Martin T. Vechev |
| 2025 | Average Sensitivity of Hierarchical k-Median Clustering. Shijie Li, Weiqiang He, Ruobing Bai, Pan Peng |
| 2025 | Avoiding Catastrophe in Online Learning by Asking for Help. Benjamin Plaut, Hanlin Zhu, Stuart Russell |
| 2025 | Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts. Mateo Espinosa Zarlenga, Gabriele Dominici, Pietro Barbiero, Zohreh Shams, Mateja Jamnik |
| 2025 | Avoiding spurious sharpness minimization broadens applicability of SAM. Sidak Pal Singh, Hossein Mobahi, Atish Agarwala, Yann N. Dauphin |
| 2025 | AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders. Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christopher D. Manning, Christopher Potts |
| 2025 | B-score: Detecting biases in large language models using response history. An Vo, Mohammad Reza Taesiri, Daeyoung Kim, Anh Totti Nguyen |
| 2025 | BAME: Block-Aware Mask Evolution for Efficient N: M Sparse Training. Chenyi Yang, Wenjie Nie, Yuxin Zhang, Yuhang Wu, Xiawu Zheng, Guannan Jiang, Rongrong Ji |
| 2025 | BARK: A Fully Bayesian Tree Kernel for Black-box Optimization. Toby Boyne, Jose Pablo Folch, Robert M. Lee, Behrang Shafei, Ruth Misener |
| 2025 | BARNN: A Bayesian Autoregressive and Recurrent Neural Network. Dario Coscia, Max Welling, Nicola Demo, Gianluigi Rozza |
| 2025 | BAnG: Bidirectional Anchored Generation for Conditional RNA Design. Roman Klypa, Alberto Bietti, Sergei Grudinin |
| 2025 | BCE vs. CE in Deep Feature Learning. Qiufu Li, Huibin Xiao, Linlin Shen |
| 2025 | BDC-CLIP: Brownian Distance Covariance for Adapting CLIP to Action Recognition. Fei Long, Xiaoou Li, Jiaming Lv, Haoyuan Yang, Xianjun Cheng, Peihua Li |
| 2025 | BECAME: Bayesian Continual Learning with Adaptive Model Merging. Mei Li, Yuxiang Lu, Qinyan Dai, Suizhi Huang, Yue Ding, Hongtao Lu |
| 2025 | BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute. Dujian Ding, Ankur Mallick, Shaokun Zhang, Chi Wang, Daniel Madrigal, Mirian del Carmen Hipolito Garcia, Menglin Xia, Laks V. S. Lakshmanan, Qingyun Wu, Victor Rühle |
| 2025 | BILBO: BILevel Bayesian Optimization. Wan Theng Ruth Chew, Quoc Phong Nguyen, Bryan Kian Hsiang Low |
| 2025 | BOOD: Boundary-based Out-Of-Distribution Data Generation. Qilin Liao, Shuo Yang, Bo Zhao, Ping Luo, Hengshuang Zhao |
| 2025 | BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization. Zijun Liao, Jinbiao Chen, Debing Wang, Zizhen Zhang, Jiahai Wang |
| 2025 | BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling. Hao Li, Yu-Hao Huang, Chang Xu, Viktor Schlegel, Renhe Jiang, Riza Batista-Navarro, Goran Nenadic, Jiang Bian |
| 2025 | BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning. Han Zhong, Yutong Yin, Shenao Zhang, Xiaojun Xu, Yuanxin Liu, Yifei Zuo, Zhihan Liu, Boyi Liu, Sirui Zheng, Hongyi Guo, Liwei Wang, Mingyi Hong, Zhaoran Wang |
| 2025 | BSLoRA: Enhancing the Parameter Efficiency of LoRA with Intra-Layer and Inter-Layer Sharing. Yuhua Zhou, Ruifeng Li, Changhai Zhou, Fei Yang, Aimin Pan |
| 2025 | BSO: Binary Spiking Online Optimization Algorithm. Yu Liang, Yu Yang, Wenjie Wei, Ammar Belatreche, Shuai Wang, Malu Zhang, Yang Yang |
| 2025 | BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach. Haozhao Wang, Shengyu Wang, Jiaming Li, Hao Ren, Xingshuo Han, Wenchao Xu, Shangwei Guo, Tianwei Zhang, Ruixuan Li |
| 2025 | BaWA: Automatic Optimizing Pruning Metric for Large Language Models with Balanced Weight and Activation. Lian Liu, Xiandong Zhao, Guanchen Li, Dong Li, Mengdi Wang, Yinhe Han, Xiaowei Li, Ying Wang |
| 2025 | BackSlash: Rate Constrained Optimized Training of Large Language Models. Jun Wu, Jiangtao Wen, Yuxing Han |
| 2025 | Backdoor Attacks in Token Selection of Attention Mechanism. Yunjuan Wang, Raman Arora |
| 2025 | BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing. Dongliang Guo, Mengxuan Hu, Zihan Guan, Thomas Hartvigsen, Sheng Li |
| 2025 | Balanced Learning for Domain Adaptive Semantic Segmentation. Wangkai Li, Rui Sun, Bohao Liao, Zhaoyang Li, Tianzhu Zhang |
| 2025 | Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality. Joshua Southern, Yam Eitan, Guy Bar-Shalom, Michael M. Bronstein, Haggai Maron, Fabrizio Frasca |
| 2025 | Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach. Jin Zhu, Jingyi Li, Hongyi Zhou, Yinan Lin, Zhenhua Lin, Chengchun Shi |
| 2025 | Balancing Model Efficiency and Performance: Adaptive Pruner for Long-tailed Data. Zhe Zhao, Haibin Wen, Pengkun Wang, Shuang Wang, Zhenkun Wang, Qingfu Zhang, Yang Wang |
| 2025 | Balancing Preservation and Modification: A Region and Semantic Aware Metric for Instruction-Based Image Editing. Zhuoying Li, Zhu Xu, Yuxin Peng, Yang Liu |
| 2025 | Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data. Corinna Cortes, Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2025 | BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms. Yunlong Hou, Fengzhuo Zhang, Cunxiao Du, Xuan Zhang, Jiachun Pan, Tianyu Pang, Chao Du, Vincent Y. F. Tan, Zhuoran Yang |
| 2025 | Banyan: Improved Representation Learning with Explicit Structure. Mattia Opper, N. Siddharth |
| 2025 | Batch List-Decodable Linear Regression via Higher Moments. Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Sihan Liu, Thanasis Pittas |
| 2025 | BaxBench: Can LLMs Generate Correct and Secure Backends? Mark Vero, Niels Mündler, Victor Chibotaru, Veselin Raychev, Maximilian Baader, Nikola Jovanovic, Jingxuan He, Martin T. Vechev |
| 2025 | Bayesian Active Learning for Bivariate Causal Discovery. Yuxuan Wang, Mingzhou Liu, Xinwei Sun, Wei Wang, Yizhou Wang |
| 2025 | Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models. Mehmet Yigit Balik, Maksim Sinelnikov, Priscilla Ong, Harri Lähdesmäki |
| 2025 | Bayesian Inference for Correlated Human Experts and Classifiers. Markelle Kelly, Alex James Boyd, Samuel Showalter, Mark Steyvers, Padhraic Smyth |
| 2025 | Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks. Dongwoo Lee, Dong Bok Lee, Steven Adriaensen, Juho Lee, Sung Ju Hwang, Frank Hutter, Seon Joo Kim, Hae Beom Lee |
| 2025 | Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds. Aya Kayal, Sattar Vakili, Laura Toni, Da-Shan Shiu, Alberto Bernacchia |
| 2025 | Bayesian Weight Enhancement with Steady-State Adaptation for Test-time Adaptation in Dynamic Environments. Jae-Hong Lee |
| 2025 | Be Confident: Uncovering Overfitting in MLLM Multi-Task Tuning. Wenke Huang, Jian Liang, Guancheng Wan, Didi Zhu, He Li, Jiawei Shao, Mang Ye, Bo Du, Dacheng Tao |
| 2025 | Be a Goldfish: Forgetting Bad Conditioning in Sparse Linear Regression via Variational Autoencoders. Kuheli Pratihar, Debdeep Mukhopadhyay |
| 2025 | Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning. Chen-Xiao Gao, Chenyang Wu, Mingjun Cao, Chenjun Xiao, Yang Yu, Zongzhang Zhang |
| 2025 | Behavior-agnostic Task Inference for Robust Offline In-context Reinforcement Learning. Long Ma, Fangwei Zhong, Yizhou Wang |
| 2025 | Behavioral Exploration: Learning to Explore via In-Context Adaptation. Andrew Wagenmaker, Zhiyuan Zhou, Sergey Levine |
| 2025 | Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation. Taehyun Cho, Seungyub Han, Seokhun Ju, Dohyeong Kim, Kyungjae Lee, Jungwoo Lee |
| 2025 | Benchmarking Abstract and Reasoning Abilities Through A Theoretical Perspective. Qingchuan Ma, Yuhang Wu, Xiawu Zheng, Rongrong Ji |
| 2025 | Benchmarking Quantum Reinforcement Learning. Nico Meyer, Christian Ufrecht, George Yammine, Georgios D. Kontes, Christopher Mutschler, Daniel D. Scherer |
| 2025 | Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression. Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu |
| 2025 | Benign Overfitting in Token Selection of Attention Mechanism. Keitaro Sakamoto, Issei Sato |
| 2025 | Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety. Zihan Guan, Mengxuan Hu, Ronghang Zhu, Sheng Li, Anil Vullikanti |
| 2025 | Best Subset Selection: Optimal Pursuit for Feature Selection and Elimination. Zhihan Zhu, Yanhao Zhang, Yong Xia |
| 2025 | Best of Both Worlds: Advantages of Hybrid Graph Sequence Models. Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford, Bryan Perozzi, Vahab Mirrokni |
| 2025 | Best of Both Worlds: Regret Minimization versus Minimax Play. Adrian Müller, Jon Schneider, Stratis Skoulakis, Luca Viano, Volkan Cevher |
| 2025 | Better to Teach than to Give: Domain Generalized Semantic Segmentation via Agent Queries with Diffusion Model Guidance. Fan Li, Xuan Wang, Min Qi, Zhaoxiang Zhang, Yuelei Xu |
| 2025 | Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling. Shuqi Lu, Xiaohong Ji, Bohang Zhang, Lin Yao, Siyuan Liu, Zhifeng Gao, Linfeng Zhang, Guolin Ke |
| 2025 | Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignment. Yifan Zhang, Ge Zhang, Yue Wu, Kangping Xu, Quanquan Gu |
| 2025 | Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning. Mehrdad Moghimi, Hyejin Ku |
| 2025 | Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning. Ze'ev Zukerman, Bassel Hamoud, Kfir Yehuda Levy |
| 2025 | Beyond Confidence: Exploiting Homogeneous Pattern for Semi-Supervised Semantic Segmentation. Rui Sun, Huayu Mai, Wangkai Li, Yujia Chen, Naisong Luo, Yuan Wang, Tianzhu Zhang |
| 2025 | Beyond Cropped Regions: New Benchmark and Corresponding Baseline for Chinese Scene Text Retrieval in Diverse Layouts. Gengluo Li, Huawen Shen, Yu Zhou |
| 2025 | Beyond Entropy: Region Confidence Proxy for Wild Test-Time Adaptation. Zixuan Hu, Yichun Hu, Xiaotong Li, Shixiang Tang, Lingyu Duan |
| 2025 | Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence. Gouki Minegishi, Hiroki Furuta, Shohei Taniguchi, Yusuke Iwasawa, Yutaka Matsuo |
| 2025 | Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2-distance. Marta Gentiloni Silveri, Antonio Ocello |
| 2025 | Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning. Le-Trung Nguyen, Aël Quélennec, Van-Tam Nguyen, Enzo Tartaglione |
| 2025 | Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation. Tiansheng Wen, Yifei Wang, Zequn Zeng, Zhong Peng, Yudi Su, Xinyang Liu, Bo Chen, Hongwei Liu, Stefanie Jegelka, Chenyu You |
| 2025 | Beyond Message Passing: Neural Graph Pattern Machine. Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye |
| 2025 | Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity. Quan M. Nguyen, Nishant A. Mehta, Cristóbal Guzmán |
| 2025 | Beyond One-Hot Labels: Semantic Mixing for Model Calibration. Haoyang Luo, Linwei Tao, Minjing Dong, Chang Xu |
| 2025 | Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms? Yaolong Yu, Fan Yao, Sinno Jialin Pan |
| 2025 | Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs. Jie Hu, Yi-Ting Ma, Do Young Eun |
| 2025 | Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions. Eray Erturk, Fahad Kamran, Salar Abbaspourazad, Sean Jewell, Harsh Sharma, Yujie Li, Sinead Williamson, Nicholas J. Foti, Joseph Futoma |
| 2025 | Beyond Task-Specific Reasoning: A Unified Conditional Generative Framework for Abstract Visual Reasoning. Fan Shi, Bin Li, Xiangyang Xue |
| 2025 | Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC. Tyler Clark, Mark Towers, Christine Evers, Jonathon Hare |
| 2025 | Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective. Steve Azzolin, Sagar Malhotra, Andrea Passerini, Stefano Teso |
| 2025 | Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics. Shiwei Li, Xiandi Luo, Xing Tang, Haozhao Wang, Hao Chen, Weihong Luo, Yuhua Li, Xiuqiang He, Ruixuan Li |
| 2025 | Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion. Binchi Zhang, Zaiyi Zheng, Zhengzhang Chen, Jundong Li |
| 2025 | Bi-perspective Splitting Defense: Achieving Clean-Seed-Free Backdoor Security. Yangyang Shen, Xiao Tan, Dian Shen, Meng Wang, Beilun Wang |
| 2025 | BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly. Yan Shen, Ruihai Wu, Yubin Ke, Xinyuan Song, Zeyi Li, Xiaoqi Li, Hongwei Fan, Haoran Lu, Hao Dong |
| 2025 | BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution. Kai Liu, Kaicheng Yang, Zheng Chen, Zhiteng Li, Yong Guo, Wenbo Li, Linghe Kong, Yulun Zhang |
| 2025 | BiMark: Unbiased Multilayer Watermarking for Large Language Models. Xiaoyan Feng, He Zhang, Yanjun Zhang, Leo Yu Zhang, Shirui Pan |
| 2025 | Bifurcate then Alienate: Incomplete Multi-view Clustering via Coupled Distribution Learning with Linear Overhead. Shengju Yu, Yiu-ming Cheung, Siwei Wang, Xinwang Liu, En Zhu |
| 2025 | Binary Hypothesis Testing for Softmax Models and Leverage Score Models. Yuzhou Gu, Zhao Song, Junze Yin |
| 2025 | BinauralFlow: A Causal and Streamable Approach for High-Quality Binaural Speech Synthesis with Flow Matching Models. Susan Liang, Dejan Markovic, Israel D. Gebru, Steven Krenn, Todd Keebler, Jacob Sandakly, Frank Yu, Samuel Hassel, Chenliang Xu, Alexander Richard |
| 2025 | Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation. Michal Lukasik, Lin Chen, Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Felix X. Yu, Sashank J. Reddi, Gang Fu, MohammadHossein Bateni, Sanjiv Kumar |
| 2025 | Bivariate Causal Discovery with Proxy Variables: Integral Solving and Beyond. Yong Wu, Yanwei Fu, Shouyan Wang, Xinwei Sun |
| 2025 | Black-Box Adversarial Attacks on LLM-Based Code Completion. Slobodan Jenko, Niels Mündler, Jingxuan He, Mark Vero, Martin T. Vechev |
| 2025 | Blink of an eye: a simple theory for feature localization in generative models. Marvin Li, Aayush Karan, Sitan Chen |
| 2025 | BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference. Wonsuk Jang, Thierry Tambe |
| 2025 | BoA: Attention-aware Post-training Quantization without Backpropagation. Junhan Kim, Ho-Young Kim, Eulrang Cho, Chungman Lee, Joonyoung Kim, Yongkweon Jeon |
| 2025 | Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad? Antonia Wüst, Tim Nelson Tobiasch, Lukas Helff, Inga Ibs, Wolfgang Stammer, Devendra Singh Dhami, Constantin A. Rothkopf, Kristian Kersting |
| 2025 | Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation. Soobin Um, Beomsu Kim, Jong Chul Ye |
| 2025 | Boosting Adversarial Robustness with CLAT: Criticality Leveraged Adversarial Training. Bhavna Gopal, Huanrui Yang, Jingyang Zhang, Mark Horton, Yiran Chen |
| 2025 | Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners. Manh Pham Hung, Aaqib Saeed, Dong Ma |
| 2025 | Boosting Multi-Domain Fine-Tuning of Large Language Models through Evolving Interactions between Samples. Xize Liang, Lin Yang, Jie Wang, Yiyang Lu, Runyu Wu, Hanzhu Chen, Jianye Hao |
| 2025 | Boosting Protein Graph Representations through Static-Dynamic Fusion. Pengkang Guo, Bruno E. Correia, Pierre Vandergheynst, Daniel Probst |
| 2025 | Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark. Bingchen Miao, Yang Wu, Minghe Gao, Qifan Yu, Wendong Bu, Wenqiao Zhang, Yunfei Li, Siliang Tang, Tat-Seng Chua, Juncheng Li |
| 2025 | Bootstrapping Self-Improvement of Language Model Programs for Zero-Shot Schema Matching. Nabeel Seedat, Mihaela van der Schaar |
| 2025 | BounDr.E: Predicting Drug-likeness via Biomedical Knowledge Alignment and EM-like One-Class Boundary Optimization. Dongmin Bang, Inyoung Sung, Yinhua Piao, Sangseon Lee, Sun Kim |
| 2025 | Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time. Mohamad Fares El Hajj Chehade, Soumya Suvra Ghosal, Souradip Chakraborty, Avinash Reddy, Dinesh Manocha, Hao Zhu, Amrit Singh Bedi |
| 2025 | BoxLM: Unifying Structures and Semantics of Medical Concepts for Diagnosis Prediction in Healthcare. Yanchao Tan, Hang Lv, Yunfei Zhan, Guofang Ma, Bo Xiong, Carl Yang |
| 2025 | Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO. Ayman Chaouki, Jesse Read, Albert Bifet |
| 2025 | Breaking Barriers: Combinatorial Algorithms for Non-Monotone Submodular Maximization with Sublinear Adaptivity and 1/e Approximation. Yixin Chen, Wenjing Chen, Alan Kuhnle |
| 2025 | Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting. Zhining Liu, Ze Yang, Xiao Lin, Ruizhong Qiu, Tianxin Wei, Yada Zhu, Hendrik F. Hamann, Jingrui He, Hanghang Tong |
| 2025 | Breaking the Barrier of Hard Samples: A Data-Centric Approach to Synthetic Data for Medical Tasks. Maynara Donato de Souza, Cleber Zanchettin |
| 2025 | Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning. Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman |
| 2025 | Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries. Edith Cohen, Mihir Singhal, Uri Stemmer |
| 2025 | Breaking the n1.5 Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition. Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrovic, Yuriy Nevmyvaka, Sandeep Silwal, Yinzhan Xu |
| 2025 | Bridging Fairness and Efficiency in Conformal Inference: A Surrogate-Assisted Group-Clustered Approach. Chenyin Gao, Peter B. Gilbert, Larry Han |
| 2025 | Bridging Layout and RTL: Knowledge Distillation based Timing Prediction. Mingjun Wang, Yihan Wen, Bin Sun, Jianan Mu, Juan Li, Xiaoyi Wang, Jing Justin Ye, Bei Yu, Huawei Li |
| 2025 | Bridging Protein Sequences and Microscopy Images with Unified Diffusion Models. Dihan Zheng, Bo Huang |
| 2025 | Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging. Shiqi Chen, Jinghan Zhang, Tongyao Zhu, Wei Liu, Siyang Gao, Miao Xiong, Manling Li, Junxian He |
| 2025 | Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition. Jaeheun Jung, Jaehyuk Lee, Chang-Hae Jung, Hanyoung Kim, Bosung Jung, Donghun Lee |
| 2025 | Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning. Ankit Pratap Singh, Ahmed Ali Abbasi, Namrata Vaswani |
| 2025 | C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation. Guoxin Chen, Minpeng Liao, Peiying Yu, Dingmin Wang, Zile Qiao, Chao Yang, Xin Zhao, Kai Fan |
| 2025 | C2IQL: Constraint-Conditioned Implicit Q-learning for Safe Offline Reinforcement Learning. Zifan Liu, Xinran Li, Jun Zhang |
| 2025 | CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging. Zongzhen Yang, Binhang Qi, Hailong Sun, Wenrui Long, Ruobing Zhao, Xiang Gao |
| 2025 | CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation. Aditya Gorla, Ryan Wang, Zhengtong Liu, Ulzee An, Sriram Sankararaman |
| 2025 | CAD-Editor: A Locate-then-Infill Framework with Automated Training Data Synthesis for Text-Based CAD Editing. Yu Yuan, Shizhao Sun, Qi Liu, Jiang Bian |
| 2025 | CALM: Consensus-Aware Localized Merging for Multi-Task Learning. Kunda Yan, Min Zhang, Sen Cui, Zikun Qu, Bo Jiang, Feng Liu, Changshui Zhang |
| 2025 | CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning. Xuankun Rong, Jianshu Zhang, Kun He, Mang Ye |
| 2025 | CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models. Guangzhi Sun, Xiao Zhan, Shutong Feng, Philip C. Woodland, Jose Such |
| 2025 | CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging. Wenju Sun, Qingyong Li, Yangliao Geng, Boyang Li |
| 2025 | CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models. Sen Peng, Mingyue Wang, Jianfei He, Jijia Yang, Xiaohua Jia |
| 2025 | CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition. Zebin Wang, Menghan Lin, Bolin Shen, Ken Anderson, Molei Liu, Tianxi Cai, Yushun Dong |
| 2025 | CERTAIN: Context Uncertainty-aware One-Shot Adaptation for Context-based Offline Meta Reinforcement Learning. Hongtu Zhou, Ruiling Yang, Yakun Zhu, Haoqi Zhao, Hai Zhang, Di Zhang, Junqiao Zhao, Chen Ye, Changjun Jiang |
| 2025 | CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models. Junbo Yin, Chao Zha, Wenjia He, Chencheng Xu, Xin Gao |
| 2025 | CFPT: Empowering Time Series Forecasting through Cross-Frequency Interaction and Periodic-Aware Timestamp Modeling. Feifei Kou, Jiahao Wang, Lei Shi, Yuhan Yao, Yawen Li, Suguo Zhu, Zhongbao Zhang, Junping Du |
| 2025 | CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation. Minghao Fu, Guo-Hua Wang, Liangfu Cao, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang |
| 2025 | CLARIFY: Contrastive Preference Reinforcement Learning for Untangling Ambiguous Queries. Ni Mu, Hao Hu, Xiao Hu, Yiqin Yang, Bo Xu, Qing-Shan Jia |
| 2025 | CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models. Wei Dai, Peilin Chen, Malinda Lu, Daniel Li, Haowen Wei, Hejie Cui, Paul Pu Liang |
| 2025 | CLOVER: Cross-Layer Orthogonal Vectors Pruning. Fanxu Meng, Pingzhi Tang, Fan Jiang, Muhan Zhang |
| 2025 | CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations. Haotian Si, Changhua Pei, Jianhui Li, Dan Pei, Gaogang Xie |
| 2025 | COExpander: Adaptive Solution Expansion for Combinatorial Optimization. Jiale Ma, Wenzheng Pan, Yang Li, Junchi Yan |
| 2025 | COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning. Chamika Sudusinghe, Gerasimos Gerogiannis, Damitha Lenadora, Charles Block, Josep Torrellas, Charith Mendis |
| 2025 | COKE: Core Kernel for More Efficient Approximation of Kernel Weights in Multiple Kernel Clustering. Weixuan Liang, Xinwang Liu, Ke Liang, Jiyuan Liu, En Zhu |
| 2025 | COMRECGC: Global Graph Counterfactual Explainer through Common Recourse. Gregoire Fournier, Sourav Medya |
| 2025 | COSDA: Counterfactual-based Susceptibility Risk Framework for Open-Set Domain Adaptation. Wenxu Wang, Rui Zhou, Jing Wang, Yun Zhou, Cheng Zhu, Ruichun Tang, Bo Han, Nevin L. Zhang |
| 2025 | CPCF: A Cross-Prompt Contrastive Framework for Referring Multimodal Large Language Models. Lanyun Zhu, Deyi Ji, Tianrun Chen, Haiyang Wu, De Wen Soh, Jun Liu |
| 2025 | CRANE: Reasoning with constrained LLM generation. Debangshu Banerjee, Tarun Suresh, Shubham Ugare, Sasa Misailovic, Gagandeep Singh |
| 2025 | CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization. Nay Myat Min, Long H. Pham, Yige Li, Jun Sun |
| 2025 | CSG-ODE: ControlSynth Graph ODE For Modeling Complex Evolution of Dynamic Graphs. Zhiqiang Wang, Xiaoyi Wang, Jianqing Liang |
| 2025 | CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features. Xiaokun Feng, Dailing Zhang, Shiyu Hu, Xuchen Li, Meiqi Wu, Jing Zhang, Xiaotang Chen, Kaiqi Huang |
| 2025 | CSV-Occ: Fusing Multi-frame Alignment for Occupancy Prediction with Temporal Cross State Space Model and Central Voting Mechanism. Ziming Zhu, Yu Zhu, Jiahao Chen, Xiaofeng Ling, Huanlei Chen, Lihua Sun |
| 2025 | CTBench: A Library and Benchmark for Certified Training. Yuhao Mao, Stefan Balauca, Martin T. Vechev |
| 2025 | CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty. Harry Zhang, Luca Carlone |
| 2025 | CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities. Yuxuan Zhu, Antony Kellermann, Dylan Bowman, Philip Li, Akul Gupta, Adarsh Danda, Richard Fang, Conner Jensen, Eric Ihli, Jason Benn, Jet Geronimo, Avi Dhir, Sudhit Rao, Kaicheng Yu, Twm Stone, Daniel Kang |
| 2025 | Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing. Kaifeng Gao, Jiaxin Shi, Hanwang Zhang, Chunping Wang, Jun Xiao, Long Chen |
| 2025 | CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention. Han Li, Fei Liu, Zhi Zheng, Yu Zhang, Zhenkun Wang |
| 2025 | Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models. Alina Shutova, Vladimir Malinovskii, Vage Egiazarian, Denis Kuznedelev, Denis Mazur, Nikita Surkov, Ivan Ermakov, Dan Alistarh |
| 2025 | Calibrated Language Models and How to Find Them with Label Smoothing. Jerry Huang, Peng Lu, Qiuhao Zeng |
| 2025 | Calibrated Physics-Informed Uncertainty Quantification. Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi, Timothy Nunn, Daniel Giles, Matt J. Kusner, Stanislas Pamela, Marc Peter Deisenroth |
| 2025 | Calibrated Value-Aware Model Learning with Probabilistic Environment Models. Claas Voelcker, Anastasiia Pedan, Arash Ahmadian, Romina Abachi, Igor Gilitschenski, Amir-massoud Farahmand |
| 2025 | Calibrating Video Watch-time Predictions with Credible Prototype Alignment. Chao Cui, Shisong Tang, Fan Li, Jiechao Gao, Hechang Chen |
| 2025 | Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example. Yuhan Helena Liu, Guangyu Robert Yang, Christopher J. Cueva |
| 2025 | Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence. Yuankai Luo, Lei Shi, Xiao-Ming Wu |
| 2025 | Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression. Peijie Dong, Zhenheng Tang, Xiang Liu, Lujun Li, Xiaowen Chu, Bo Li |
| 2025 | Can DBNNs Robust to Environmental Noise for Resource-constrained Scenarios? Wendong Zheng, Junyang Chen, Husheng Guo, Wenjian Wang |
| 2025 | Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? Yujin Han, Andi Han, Wei Huang, Chaochao Lu, Difan Zou |
| 2025 | Can Large Language Models Understand Intermediate Representations in Compilers? Hailong Jiang, Jianfeng Zhu, Yao Wan, Bo Fang, Hongyu Zhang, Ruoming Jin, Qiang Guan |
| 2025 | Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark. Yunzhuo Hao, Jiawei Gu, Huichen Will Wang, Linjie Li, Zhengyuan Yang, Lijuan Wang, Yu Cheng |
| 2025 | Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective. Jiawei Huang, Bingcong Li, Christoph Dann, Niao He |
| 2025 | Can Transformers Learn Full Bayesian Inference in Context? Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer |
| 2025 | Can Transformers Reason Logically? A Study in SAT Solving. Leyan Pan, Vijay Ganesh, Jacob D. Abernethy, Chris Esposo, Wenke Lee |
| 2025 | Can We Predict Performance of Large Models across Vision-Language Tasks? Qinyu Zhao, Ming Xu, Kartik Gupta, Akshay Asthana, Liang Zheng, Stephen Gould |
| 2025 | Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs. Haoming Yang, Ke Ma, Xiaojun Jia, Yingfei Sun, Qianqian Xu, Qingming Huang |
| 2025 | Canonical Rank Adaptation: An Efficient Fine-Tuning Strategy for Vision Transformers. Lokesh Veeramacheneni, Moritz Wolter, Hilde Kuehne, Juergen Gall |
| 2025 | Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy. Haoqi Wu, Wei Dai, Li Wang, Qiang Yan |
| 2025 | Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation. Jan Pauls, Max Zimmer, Berkant Turan, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Fabian Gieseke |
| 2025 | Catch Your Emotion: Sharpening Emotion Perception in Multimodal Large Language Models. Yiyang Fang, Jian Liang, Wenke Huang, He Li, Kehua Su, Mang Ye |
| 2025 | Catching Two Birds with One Stone: Reward Shaping with Dual Random Networks for Balancing Exploration and Exploitation. Haozhe Ma, Fangling Li, Jing Yu Lim, Zhengding Luo, Thanh Vinh Vo, Tze-Yun Leong |
| 2025 | CateKV: On Sequential Consistency for Long-Context LLM Inference Acceleration. Haoyun Jiang, Haolin Li, Jianwei Zhang, Fei Huang, Qiang Hu, Minmin Sun, Shuai Xiao, Yong Li, Junyang Lin, Jiangchao Yao |
| 2025 | Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics. Tyler Kastner, Mark Rowland, Yunhao Tang, Murat A. Erdogdu, Amir-massoud Farahmand |
| 2025 | Categorical Schrödinger Bridge Matching. Grigoriy Ksenofontov, Alexander Korotin |
| 2025 | Catoni Contextual Bandits are Robust to Heavy-tailed Rewards. Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang |
| 2025 | Causal Abstraction Inference under Lossy Representations. Kevin Muyuan Xia, Elias Bareinboim |
| 2025 | Causal Abstraction Learning based on the Semantic Embedding Principle. Gabriele D'Acunto, Fabio Massimo Zennaro, Yorgos Felekis, Paolo Di Lorenzo |
| 2025 | Causal Attribution Analysis for Continuous Outcomes. Shanshan Luo, Yixuan Yu, Chunchen Liu, Feng Xie, Zhi Geng |
| 2025 | Causal Discovery from Conditionally Stationary Time Series. Carles Balsells Rodas, Xavier Sumba, Tanmayee Narendra, Ruibo Tu, Gabriele Beate Schweikert, Hedvig Kjellström, Yingzhen Li |
| 2025 | Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants. Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Negar Kiyavash, Mathias Drton |
| 2025 | Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning. Minqi Yu, Jinduo Liu, Junzhong Ji |
| 2025 | Causal Logistic Bandits with Counterfactual Fairness Constraints. Jiajun Chen, Jin Tian, Christopher John Quinn |
| 2025 | Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel. Carlota Parés-Morlans, Michelle Yi, Claire Chen, Sarah A. Wu, Rika Antonova, Tobias Gerstenberg, Jeannette Bohg |
| 2025 | Causality Inspired Federated Learning for OOD Generalization. Jiayuan Zhang, Xuefeng Liu, Jianwei Niu, Shaojie Tang, Haotian Yang, Xinghao Wu |
| 2025 | Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection. HyunGi Kim, Jisoo Mok, Dongjun Lee, Jaihyun Lew, Sungjae Kim, Sungroh Yoon |
| 2025 | Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention. Dejia Xu, Yifan Jiang, Chen Huang, Liangchen Song, Thorsten Gernoth, Liangliang Cao, Zhangyang Wang, Hao Tang |
| 2025 | CellFlux: Simulating Cellular Morphology Changes via Flow Matching. Yuhui Zhang, Yuchang Su, Chenyu Wang, Tianhong Li, Zoe Wefers, Jeffrey J. Nirschl, James Burgess, Daisy Ding, Alejandro Lozano, Emma Lundberg, Serena Yeung-Levy |
| 2025 | Censor Dependent Variational Inference. Chuanhui Liu, Xiao Wang |
| 2025 | Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts. Amir Najafi, Samin Mahdizadeh Sani, Farzan Farnia |
| 2025 | Certification for Differentially Private Prediction in Gradient-Based Training. Matthew Wicker, Philip Sosnin, Igor Shilov, Adrianna Janik, Mark Niklas Müller, Yves-Alexandre de Montjoye, Adrian Weller, Calvin Tsay |
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| 2025 | Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off. Yuecheng Li, Lele Fu, Tong Wang, Jian Lou, Bin Chen, Lei Yang, Jian Shen, Zibin Zheng, Chuan Chen |
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| 2025 | Closed-Loop Long-Horizon Robotic Planning via Equilibrium Sequence Modeling. Jinghan Li, Zhicheng Sun, Yadong Mu |
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| 2025 | CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization. Dasol Hong, Wooju Lee, Hyun Myung |
| 2025 | CoDy: Counterfactual Explainers for Dynamic Graphs. Zhan Qu, Daniel Gomm, Michael Färber |
| 2025 | CoMemo: LVLMs Need Image Context with Image Memory. Shi Liu, Weijie Su, Xizhou Zhu, Wenhai Wang, Jifeng Dai |
| 2025 | CoPINN: Cognitive Physics-Informed Neural Networks. Siyuan Duan, Wenyuan Wu, Peng Hu, Zhenwen Ren, Dezhong Peng, Yuan Sun |
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| 2025 | Code-Generated Graph Representations Using Multiple LLM Agents for Material Properties Prediction. Jiao Huang, Qianli Xing, Jinglong Ji, Bo Yang |
| 2025 | CodeIO: Condensing Reasoning Patterns via Code Input-Output Prediction. Junlong Li, Daya Guo, Dejian Yang, Runxin Xu, Yu Wu, Junxian He |
| 2025 | CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance. Yongchao Chen, Yilun Hao, Yueying Liu, Yang Zhang, Chuchu Fan |
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| 2025 | Collapse-Proof Non-Contrastive Self-Supervised Learning. Emanuele Sansone, Tim Lebailly, Tinne Tuytelaars |
| 2025 | CombiMOTS: Combinatorial Multi-Objective Tree Search for Dual-Target Molecule Generation. Thibaud Southiratn, Bonil Koo, Yijingxiu Lu, Sun Kim |
| 2025 | Combinatorial Reinforcement Learning with Preference Feedback. Joongkyu Lee, Min-hwan Oh |
| 2025 | Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation. Zhan Zhuang, Xiequn Wang, Wei Li, Yulong Zhang, Qiushi Huang, Shuhao Chen, Xuehao Wang, Yanbin Wei, Yuhe Nie, Kede Ma, Yu Zhang, Ying Wei |
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| 2025 | Communicating Activations Between Language Model Agents. Vignav Ramesh, Kenneth Li |
| 2025 | Commute Graph Neural Networks. Wei Zhuo, Han Yu, Guang Tan, Xiaoxiao Li |
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| 2025 | Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries. Xuening Feng, Zhaohui Jiang, Timo Kaufmann, Eyke Hüllermeier, Paul Weng, Yifei Zhu |
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| 2025 | Competing Bandits in Matching Markets via Super Stability. Soumya Basu |
| 2025 | Competitively Consistent Clustering. Niv Buchbinder, Roie Levin, Yue Yang |
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| 2025 | Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation. Renhao Lu |
| 2025 | Componential Prompt-Knowledge Alignment for Domain Incremental Learning. Kunlun Xu, Xu Zou, Gang Hua, Jiahuan Zhou |
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| 2025 | Compositional Condition Question Answering in Tabular Understanding. Jun-Peng Jiang, Tao Zhou, De-Chuan Zhan, Han-Jia Ye |
| 2025 | Compositional Flows for 3D Molecule and Synthesis Pathway Co-design. Tony Shen, Seonghwan Seo, Ross Irwin, Kieran Didi, Simon Olsson, Woo Youn Kim, Martin Ester |
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| 2025 | Compressing tree ensembles through Level-wise Optimization and Pruning. Laurens Devos, Timo Martens, Deniz Can Oruc, Wannes Meert, Hendrik Blockeel, Jesse Davis |
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| 2025 | Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders. Charles O'Neill, Alim Gumran, David A. Klindt |
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| 2025 | Computing Voting Rules with Improvement Feedback. Evi Micha, Vasilis Varsamis |
| 2025 | ConText: Driving In-context Learning for Text Removal and Segmentation. Fei Zhang, Pei Zhang, Baosong Yang, Fei Huang, Yanfeng Wang, Ya Zhang |
| 2025 | Concentration Distribution Learning from Label Distributions. Jiawei Tang, Yuheng Jia |
| 2025 | Concept Reachability in Diffusion Models: Beyond Dataset Constraints. Marta Aparicio Rodriguez, Xenia Miscouridou, Anastasia Borovykh |
| 2025 | Concept-Based Unsupervised Domain Adaptation. Xinyue Xu, Yueying Hu, Hui Tang, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li |
| 2025 | Concept-Centric Token Interpretation for Vector-Quantized Generative Models. Tianze Yang, Yucheng Shi, Mengnan Du, Xuansheng Wu, Qiaoyu Tan, Jin Sun, Ninghao Liu |
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| 2025 | Conditional Diffusion Model with Nonlinear Data Transformation for Time Series Forecasting. J. Rishi, GVS Mothish, Deepak Subramani |
| 2025 | Conditioning Diffusions Using Malliavin Calculus. Jakiw Pidstrigach, Elizabeth Louise Baker, Carles Domingo-Enrich, George Deligiannidis, Nikolas Nüsken |
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| 2025 | Conformal Prediction as Bayesian Quadrature. Jake C. Snell, Thomas L. Griffiths |
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| 2025 | Conformity Score Averaging for Classification. Rui Luo, Zhixin Zhou |
| 2025 | Confounder-Free Continual Learning via Recursive Feature Normalization. Yash Shah, Camila González, Mohammad H. Abbasi, Qingyu Zhao, Kilian M. Pohl, Ehsan Adeli |
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| 2025 | Consensus Based Stochastic Optimal Control. Liyao Lyu, Jingrun Chen |
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| 2025 | Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability. Michael Crawshaw, Blake Woodworth, Mingrui Liu |
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| 2025 | Constrained Exploitability Descent: An Offline Reinforcement Learning Method for Finding Mixed-Strategy Nash Equilibrium. Runyu Lu, Yuanheng Zhu, Dongbin Zhao |
| 2025 | Constrained Online Convex Optimization with Polyak Feasibility Steps. Spencer Hutchinson, Mahnoosh Alizadeh |
| 2025 | Constrained Pareto Set Identification with Bandit Feedback. Cyrille Kone, Emilie Kaufmann, Laura Richert |
| 2025 | Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel Images. Zhengrui Guo, Qichen Sun, Jiabo Ma, Lishuang Feng, Jinzhuo Wang, Hao Chen |
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| 2025 | Contextual Bandits for Unbounded Context Distributions. Puning Zhao, Rongfei Fan, Shaowei Wang, Li Shen, Qixin Zhang, Zong Ke, Tianhang Zheng |
| 2025 | Contextual Linear Bandits with Delay as Payoff. Mengxiao Zhang, Yingfei Wang, Haipeng Luo |
| 2025 | Contextual Online Decision Making with Infinite-Dimensional Functional Regression. Haichen Hu, Rui Ai, Stephen Bates, David Simchi-Levi |
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| 2025 | Contextures: Representations from Contexts. Runtian Zhai, Kai Yang, Burak Varici, Che-Ping Tsai, J. Zico Kolter, Pradeep Kumar Ravikumar |
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| 2025 | Continuous Semi-Implicit Models. Longlin Yu, Jiajun Zha, Tong Yang, Tianyu Xie, Xiangyu Zhang, S.-H. Gary Chan, Cheng Zhang |
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| 2025 | Contrastive Learning with Simplicial Convolutional Networks for Short-Text Classification. Huang Liang, Benedict Lee, Daniel Hui Loong Ng, Kelin Xia |
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| 2025 | Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making. Stelios Triantafyllou, Aleksa Sukovic, Yasaman Zolfimoselo, Goran Radanovic |
| 2025 | Counterfactual Graphical Models: Constraints and Inference. Juan D. Correa, Elias Bareinboim |
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| 2025 | Cover learning for large-scale topology representation. Luis Scoccola, Uzu Lim, Heather A. Harrington |
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| 2025 | Cowpox: Towards the Immunity of VLM-based Multi-Agent Systems. Yutong Wu, Jie Zhang, Yiming Li, Chao Zhang, Qing Guo, Han Qiu, Nils Lukas, Tianwei Zhang |
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| 2025 | Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing. Xiang Zhang, Jiaqi Wei, Zijie Qiu, Sheng Xu, Nanqing Dong, Zhiqiang Gao, Siqi Sun |
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| 2025 | Curvature Enhanced Data Augmentation for Regression. Ilya Kaufman, Omri Azencot |
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| 2025 | D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples. Zijing Hu, Fengda Zhang, Kun Kuang |
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| 2025 | DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts. Tobias Braun, Mark Rothermel, Marcus Rohrbach, Anna Rohrbach |
| 2025 | DIME: Diffusion-Based Maximum Entropy Reinforcement Learning. Onur Celik, Zechu Li, Denis Blessing, Ge Li, Daniel Palenicek, Jan Peters, Georgia Chalvatzaki, Gerhard Neumann |
| 2025 | DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning. Gaoyue Zhou, Hengkai Pan, Yann LeCun, Lerrel Pinto |
| 2025 | DIS-CO: Discovering Copyrighted Content in VLMs Training Data. André V. Duarte, Xuandong Zhao, Arlindo L. Oliveira, Lei Li |
| 2025 | DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction. Rudy Morel, Jiequn Han, Edouard Oyallon |
| 2025 | DLP: Dynamic Layerwise Pruning in Large Language Models. Yuli Chen, Bo Cheng, Jiale Han, Yingying Zhang, Yingting Li, Shuhao Zhang |
| 2025 | DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing. Alexander Bienstock, Ujjwal Kumar, Antigoni Polychroniadou |
| 2025 | DMOSpeech: Direct Metric Optimization via Distilled Diffusion Model in Zero-Shot Speech Synthesis. Yinghao Aaron Li, Rithesh Kumar, Zeyu Jin |
| 2025 | DOLPHIN: A Programmable Framework for Scalable Neurosymbolic Learning. Aaditya Naik, Jason Liu, Claire Wang, Amish Sethi, Saikat Dutta, Mayur Naik, Eric Wong |
| 2025 | DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation. Yunbei Zhang, Akshay Mehra, Shuaicheng Niu, Jihun Hamm |
| 2025 | DPO Meets PPO: Reinforced Token Optimization for RLHF. Han Zhong, Zikang Shan, Guhao Feng, Wei Xiong, Xinle Cheng, Li Zhao, Di He, Jiang Bian, Liwei Wang |
| 2025 | DRAG: Data Reconstruction Attack using Guided Diffusion. Wa-Kin Lei, Jun-Cheng Chen, Shang-Tse Chen |
| 2025 | DS-VLM: Diffusion Supervision Vision Language Model. Zhen Sun, Yunhang Shen, Jie Li, Xing Sun, Pingyang Dai, Liujuan Cao, Rongrong Ji |
| 2025 | DSBRouter: End-to-end Global Routing via Diffusion Schr\"{o}dinger Bridge. Liangliang Shi, Shenhui Zhang, Xingbo Du, Nianzu Yang, Junchi Yan |
| 2025 | DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers. Xuanlei Zhao, Shenggan Cheng, Chang Chen, Zangwei Zheng, Ziming Liu, Zheming Yang, Yang You |
| 2025 | DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence. Yang Jiao, Kai Yang, Chengtao Jian |
| 2025 | DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications. Ibrahim Fayad, Max Zimmer, Martin Schwartz, Fabian Gieseke, Philippe Ciais, Gabriel Belouze, Sarah Brood, Aurélien de Truchis, Alexandre d'Aspremont |
| 2025 | DVI: A Derivative-based Vision Network for INR. Runzhao Yang, Xiaolong Wu, Zhihong Zhang, Fabian Zhang, Tingxiong Xiao, Zongren Li, Kunlun He, Jinli Suo |
| 2025 | Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models. Yuan Li, Zhengzhong Liu, Eric P. Xing |
| 2025 | Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models. Xichen Guo, Feng Xie, Yan Zeng, Hao Zhang, Zhi Geng |
| 2025 | Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development. Daoyuan Chen, Haibin Wang, Yilun Huang, Ce Ge, Yaliang Li, Bolin Ding, Jingren Zhou |
| 2025 | Data-driven Design of Randomized Control Trials with Guaranteed Treatment Effects. Santiago Cortes-Gomez, Naveen Janaki Raman, Aarti Singh, Bryan Wilder |
| 2025 | DataDecide: How to Predict Best Pretraining Data with Small Experiments. Ian Magnusson, Nguyen Tai, Ben Bogin, David Heineman, Jena D. Hwang, Luca Soldaini, Akshita Bhagia, Jiacheng Liu, Dirk Groeneveld, Oyvind Tafjord, Noah A. Smith, Pang Wei Koh, Jesse Dodge |
| 2025 | Dataflow-Guided Neuro-Symbolic Language Models for Type Inference. Ge Li, Yao Wan, Hongyu Zhang, Zhou Zhao, Wenbin Jiang, Xuanhua Shi, Hai Jin, Zheng Wang |
| 2025 | David and Goliath: Small One-step Model Beats Large Diffusion with Score Post-training. Weijian Luo, Colin Zhang, Debing Zhang, Zhengyang Geng |
| 2025 | De-AntiFake: Rethinking the Protective Perturbations Against Voice Cloning Attacks. Wei Fan, Kejiang Chen, Chang Liu, Weiming Zhang, Nenghai Yu |
| 2025 | De-coupled NeuroGF for Shortest Path Distance Approximations on Large Terrain Graphs. Samantha Chen, Pankaj K. Agarwal, Yusu Wang |
| 2025 | De-mark: Watermark Removal in Large Language Models. Ruibo Chen, Yihan Wu, Junfeng Guo, Heng Huang |
| 2025 | DeFoG: Discrete Flow Matching for Graph Generation. Yiming Qin, Manuel Madeira, Dorina Thanou, Pascal Frossard |
| 2025 | Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets. Charita Dellaporta, Patrick O'Hara, Theodoros Damoulas |
| 2025 | Decision Mixer: Integrating Long-term and Local Dependencies via Dynamic Token Selection for Decision-Making. Hongling Zheng, Li Shen, Yong Luo, Deheng Ye, Bo Du, Jialie Shen, Dacheng Tao |
| 2025 | Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents. Shayan Kiyani, George J. Pappas, Aaron Roth, Hamed Hassani |
| 2025 | Decision-aware Training of Spatiotemporal Forecasting Models to Select a Top-K Subset of Sites for Intervention. Kyle Heuton, F. Samuel Muench, Shikhar Shrestha, Thomas J. Stopka, Michael C. Hughes |
| 2025 | Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization. Junyi Liao, Zihan Zhu, Ethan X. Fang, Zhuoran Yang, Vahid Tarokh |
| 2025 | Decomposition of Graphic Design with Unified Multimodal Model. Hui Nie, Zhao Zhang, Yutao Cheng, Maoke Yang, Gonglei Shi, Qingsong Xie, Jie Shao, Xinglong Wu |
| 2025 | Decoupled SGDA for Games with Intermittent Strategy Communication. Ali Zindari, Parham Yazdkhasti, Anton Rodomanov, Tatjana Chavdarova, Sebastian U. Stich |
| 2025 | Deep Bayesian Filter for Bayes-Faithful Data Assimilation. Yuta Tarumi, Keisuke Fukuda, Shin-ichi Maeda |
| 2025 | Deep Electromagnetic Structure Design Under Limited Evaluation Budgets. Shijian Zheng, Fangxiao Jin, Shuhai Zhang, Quan Xue, Mingkui Tan |
| 2025 | Deep Fuzzy Multi-view Learning for Reliable Classification. Siyuan Duan, Yuan Sun, Dezhong Peng, Guiduo Duan, Xi Peng, Peng Hu |
| 2025 | Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer. Blake Bordelon, Cengiz Pehlevan |
| 2025 | Deep Neural Cellular Potts Models. Koen Minartz, Tim D'Hondt, Leon Hillmann, Jörn Starruß, Lutz Brusch, Vlado Menkovski |
| 2025 | Deep Principal Support Vector Machines for Nonlinear Sufficient Dimension Reduction. Yinfeng Chen, Jin Liu, Rui Qiu |
| 2025 | Deep Reinforcement Learning from Hierarchical Preference Design. Alexander Bukharin, Yixiao Li, Pengcheng He, Tuo Zhao |
| 2025 | Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines. Sho Sonoda, Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda |
| 2025 | Deep Streaming View Clustering. Honglin Yuan, Xingfeng Li, Jian Dai, Xiaojian You, Yuan Sun, Zhenwen Ren |
| 2025 | Deep Sturm-Liouville: From Sample-Based to 1D Regularization with Learnable Orthogonal Basis Functions. David Vigouroux, Joseba Dalmau, Louis Béthune, Victor Boutin |
| 2025 | Deep Unsupervised Hashing via External Guidance. Qihong Song, XitingLiu, Hongyuan Zhu, Joey Tianyi Zhou, Xi Peng, Peng Hu |
| 2025 | DeepCrossAttention: Supercharging Transformer Residual Connections. Mike Heddes, Adel Javanmard, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni |
| 2025 | DeepLayout: Learning Neural Representations of Circuit Placement Layout. Yuxiang Zhao, Zhuomin Chai, Xun Jiang, Qiang Xu, Runsheng Wang, Yibo Lin |
| 2025 | Defending LVLMs Against Vision Attacks Through Partial-Perception Supervision. Qi Zhou, Dongxia Wang, Tianlin Li, Yun Lin, Yang Liu, Jin Song Dong, Qing Guo |
| 2025 | Delay-DSGN: A Dynamic Spiking Graph Neural Network with Delay Mechanisms for Evolving Graph. Zhiqiang Wang, Jianghao Wen, Jianqing Liang |
| 2025 | Deliberation in Latent Space via Differentiable Cache Augmentation. Luyang Liu, Jonas Pfeiffer, Jiaxing Wu, Jun Xie, Arthur Szlam |
| 2025 | Delta Decompression for MoE-based LLMs Compression. Hao Gu, Wei Li, Lujun Li, Qiyuan Zhu, Mark G. Lee, Shengjie Sun, Wei Xue, Yike Guo |
| 2025 | Demeaned Sparse: Efficient Anomaly Detection by Residual Estimate. Yifan Fang, Yifei Fang, Ruizhe Chen, Haote Xu, Xinghao Ding, Yue Huang |
| 2025 | Demonstration Selection for In-Context Learning via Reinforcement Learning. Xubin Wang, Jianfei Wu, Yichen Yuan, Deyu Cai, Mingzhe Li, Weijia Jia |
| 2025 | Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector. Qirui Wu, Shizhou Zhang, De Cheng, Yinghui Xing, Di Xu, Peng Wang, Yanning Zhang |
| 2025 | Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs. Youhe Jiang, Fangcheng Fu, Xiaozhe Yao, Guoliang He, Xupeng Miao, Ana Klimovic, Bin Cui, Binhang Yuan, Eiko Yoneki |
| 2025 | Demystifying Long Chain-of-Thought Reasoning. Shiming Yang, Yuxuan Tong, Xinyao Niu, Graham Neubig, Xiang Yue |
| 2025 | Demystifying Singular Defects in Large Language Models. Haoqi Wang, Tong Zhang, Mathieu Salzmann |
| 2025 | Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation. Hongyi Zhou, Josiah P. Hanna, Jin Zhu, Ying Yang, Chengchun Shi |
| 2025 | Dendritic Localized Learning: Toward Biologically Plausible Algorithm. Changze Lv, Jingwen Xu, Yiyang Lu, Xiaohua Wang, Zhenghua Wang, Zhibo Xu, Di Yu, Xin Du, Xiaoqing Zheng, Xuanjing Huang |
| 2025 | Density Ratio Estimation with Conditional Probability Paths. Hanlin Yu, Arto Klami, Aapo Hyvärinen, Anna Korba, Omar Chehab |
| 2025 | Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning. Jungtaek Kim |
| 2025 | Dequantified Diffusion-Schrödinger Bridge for Density Ratio Estimation. Wei Chen, Shigui Li, Jiacheng Li, Junmei Yang, John Paisley, Delu Zeng |
| 2025 | Design Considerations in Offline Preference-based RL. Alekh Agarwal, Christoph Dann, Teodor Vanislavov Marinov |
| 2025 | Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling. Xiangxin Zhou, Mingyu Li, Yi Xiao, Jiahan Li, Dongyu Xue, Zaixiang Zheng, Jianzhu Ma, Quanquan Gu |
| 2025 | Detecting Strategic Deception with Linear Probes. Nicholas Goldowsky-Dill, Bilal Chughtai, Stefan Heimersheim, Marius Hobbhahn |
| 2025 | Determinant Estimation under Memory Constraints and Neural Scaling Laws. Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney |
| 2025 | Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective. Weizhong Huang, Yuxin Zhang, Xiawu Zheng, Fei Chao, Rongrong Ji |
| 2025 | Deterministic Sparse Fourier Transform for Continuous Signals with Frequency Gap. Xiaoyu Li, Zhao Song, Shenghao Xie |
| 2025 | Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models. Rafal Karczewski, Markus Heinonen, Vikas K. Garg |
| 2025 | DexScale: Automating Data Scaling for Sim2Real Generalizable Robot Control. Guiliang Liu, Yueci Deng, Runyi Zhao, Huayi Zhou, Jian Chen, Jietao Chen, Ruiyan Xu, Yunxin Tai, Kui Jia |
| 2025 | DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation. Shuyuan Wang, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni, Wei Pan |
| 2025 | DiMa: Understanding the Hardness of Online Matching Problems via Diffusion Models. Boyu Zhang, Aocheng Shen, Bing Liu, Qiankun Zhang, Bin Yuan, Jing Wang, Shenghao Liu, Xianjun Deng |
| 2025 | DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation. Dongya Jia, Zhuo Chen, Jiawei Chen, Chenpeng Du, Jian Wu, Jian Cong, Xiaobin Zhuang, Chumin Li, Zhen Wei, Yuping Wang, Yuxuan Wang |
| 2025 | Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation. Kevin Han Huang, Ni Zhan, Elif Ertekin, Peter Orbanz, Ryan P. Adams |
| 2025 | Dialogue Without Limits: Constant-Sized KV Caches for Extended Response in LLMs. Ravi Ghadia, Avinash Kumar, Gaurav Jain, Prashant J. Nair, Poulami Das |
| 2025 | Diff-MoE: Diffusion Transformer with Time-Aware and Space-Adaptive Experts. Kun Cheng, Xiao He, Lei Yu, Zhijun Tu, Mingrui Zhu, Nannan Wang, Xinbo Gao, Jie Hu |
| 2025 | DiffAdvMAP: Flexible Diffusion-Based Framework for Generating Natural Unrestricted Adversarial Examples. Zhengzhao Pan, Hua Chen, Xiaogang Zhang |
| 2025 | DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra. Montgomery Bohde, Mrunali Manjrekar, Runzhong Wang, Shuiwang Ji, Connor W. Coley |
| 2025 | Differentiable Quadratic Optimization For the Maximum Independent Set Problem. Ismail Alkhouri, Cedric Le Denmat, Yingjie Li, Cunxi Yu, Jia Liu, Rongrong Wang, Alvaro Velasquez |
| 2025 | Differentiable Solver Search for Fast Diffusion Sampling. Shuai Wang, Zexian Li, Qipeng Zhang, Tianhui Song, Xubin Li, Tiezheng Ge, Bo Zheng, Limin Wang |
| 2025 | Differentiable Structure Learning with Ancestral Constraints. Taiyu Ban, Changxin Rong, Xiangyu Wang, Lyuzhou Chen, Xin Wang, Derui Lyu, Qinrui Zhu, Huanhuan Chen |
| 2025 | Differential Coding for Training-Free ANN-to-SNN Conversion. Zihan Huang, Wei Fang, Tong Bu, Peng Xue, Zecheng Hao, Wenxuan Liu, Yuanhong Tang, Zhaofei Yu, Tiejun Huang |
| 2025 | Differential Privacy Guarantees of Markov Chain Monte Carlo Algorithms. Andrea Bertazzi, Tim Johnston, Gareth O. Roberts, Alain Oliviero Durmus |
| 2025 | Differential Privacy Under Class Imbalance: Methods and Empirical Insights. Lucas Rosenblatt, Yuliia Lut, Ethan Turok, Marco Avella Medina, Rachel Cummings |
| 2025 | Differentially Private Analysis for Binary Response Models: Optimality, Estimation, and Inference. Ce Zhang, Yixin Han, Yafei Wang, Xiaodong Yan, Linglong Kong, Ting Li, Bei Jiang |
| 2025 | Differentially Private Boxplots. Kelly Ramsay, Jairo Diaz Rodriguez |
| 2025 | Differentially Private Federated k-Means Clustering with Server-Side Data. Jonathan Scott, Christoph H. Lampert, David Saulpic |
| 2025 | Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model. Rachel Cummings, Alessandro Epasto, Jieming Mao, Tamalika Mukherjee, Tingting Ou, Peilin Zhong |
| 2025 | Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces. Kevin Rojas, Yuchen Zhu, Sichen Zhu, Felix X.-F. Ye, Molei Tao |
| 2025 | Diffusion Adversarial Post-Training for One-Step Video Generation. Shanchuan Lin, Xin Xia, Yuxi Ren, Ceyuan Yang, Xuefeng Xiao, Lu Jiang |
| 2025 | Diffusion Counterfactual Generation with Semantic Abduction. Rajat Rasal, Avinash Kori, Fabio De Sousa Ribeiro, Tian Xia, Ben Glocker |
| 2025 | Diffusion Instruction Tuning. Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Alexander Teare |
| 2025 | Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Auto Speculation. Hengyuan Hu, Aniket Das, Dorsa Sadigh, Nima Anari |
| 2025 | Diffusion Sampling Correction via Approximately 10 Parameters. Guangyi Wang, Wei Peng, Lijiang Li, Wenyu Chen, Yuren Cai, Song-Zhi Su |
| 2025 | Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors. Émile Pierret, Bruno Galerne |
| 2025 | Diffusion on Language Model Encodings for Protein Sequence Generation. Viacheslav Meshchaninov, Pavel V. Strashnov, Andrey Shevtsov, Fedor Nikolaev, Nikita Ivanisenko, Olga L. Kardymon, Dmitry P. Vetrov |
| 2025 | Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain. Gaozheng Pei, Ke Ma, Yingfei Sun, Qianqian Xu, Qingming Huang |
| 2025 | DiffusionVLA: Scaling Robot Foundation Models via Unified Diffusion and Autoregression. Junjie Wen, Yichen Zhu, Minjie Zhu, Zhibin Tang, Jinming Li, Zhongyi Zhou, Xiaoyu Liu, Chaomin Shen, Yaxin Peng, Feifei Feng |
| 2025 | Dimension-Free Adaptive Subgradient Methods with Frequent Directions. Sifan Yang, Yuanyu Wan, Peijia Li, Yibo Wang, Xiao Zhang, Zhewei Wei, Lijun Zhang |
| 2025 | Dimension-Independent Rates for Structured Neural Density Estimation. Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam |
| 2025 | Dimensionality Reduction on Complex Vector Spaces for Euclidean Distance with Dynamic Weights. Simone Moretti, Paolo Pellizzoni, Francesco Silvestri |
| 2025 | DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy. Kaixuan Xu, Jiajun Chai, Sicheng Li, Yuqian Fu, Yuanheng Zhu, Dongbin Zhao |
| 2025 | Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models. Rei Higuchi, Taiji Suzuki |
| 2025 | Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator. Kaiwen Zheng, Yongxin Chen, Huayu Chen, Guande He, Ming-Yu Liu, Jun Zhu, Qinsheng Zhang |
| 2025 | Direct Motion Models for Assessing Generated Videos. Kelsey R. Allen, Carl Doersch, Guangyao Zhou, Mohammed Suhail, Danny Driess, Ignacio Rocco, Yulia Rubanova, Thomas Kipf, Mehdi S. M. Sajjadi, Kevin Patrick Murphy, João Carreira, Sjoerd van Steenkiste |
| 2025 | Direct Prediction Set Minimization via Bilevel Conformal Classifier Training. Yuanjie Shi, Hooman Shahrokhi, Xuesong Jia, Xiongzhi Chen, Jana Doppa, Yan Yan |
| 2025 | Directed Graph Grammars for Sequence-based Learning. Michael Sun, Orion Foo, Gang Liu, Wojciech Matusik, Jie Chen |
| 2025 | Directly Forecasting Belief for Reinforcement Learning with Delays. Qingyuan Wu, Yuhui Wang, Simon Sinong Zhan, Yixuan Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Jürgen Schmidhuber, Chao Huang |
| 2025 | Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning. Vicente Balmaseda, Bokun Wang, Ching-Long Lin, Tianbao Yang |
| 2025 | Discovering Latent Causal Graphs from Spatiotemporal Data. Kun Wang, Sumanth Varambally, Duncan Watson-Parris, Yian Ma, Rose Yu |
| 2025 | Discovering Physics Laws of Dynamical Systems via Invariant Function Learning. Shurui Gui, Xiner Li, Shuiwang Ji |
| 2025 | Discovering Spoofing Attempts on Language Model Watermarks. Thibaud Gloaguen, Nikola Jovanovic, Robin Staab, Martin T. Vechev |
| 2025 | Discovering Symbolic Cognitive Models from Human and Animal Behavior. Pablo Samuel Castro, Nenad Tomasev, Ankit Anand, Navodita Sharma, Rishika Mohanta, Aparna Dev, Kuba Perlin, Siddhant Jain, Kyle Levin, Noémi Élteto, Will Dabney, Alexander Novikov, Glenn C. Turner, Maria K. Eckstein, Nathaniel D. Daw, Kevin J. Miller, Kim Stachenfeld |
| 2025 | Discovering a Zero (Zero-Vector Class of Machine Learning). Harikrishna Metta, Venkatesh Babu Radhakrishnan |
| 2025 | Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension. Yijun Dong, Yicheng Li, Yunai Li, Jason D. Lee, Qi Lei |
| 2025 | Discrepancy Minimization in Input-Sparsity Time. Yichuan Deng, Xiaoyu Li, Zhao Song, Omri Weinstein |
| 2025 | Discrete Markov Probabilistic Models: An Improved Discrete Score-Based Framework with sharp convergence bounds under minimal assumptions. Le-Tuyet-Nhi Pham, Dario Shariatian, Antonio Ocello, Giovanni Conforti, Alain Oliviero Durmus |
| 2025 | Discrete Neural Algorithmic Reasoning. Gleb Rodionov, Liudmila Prokhorenkova |
| 2025 | Discrete and Continuous Difference of Submodular Minimization. George Orfanides, Tim Hoheisel, Marwa El Halabi |
| 2025 | Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data. Siqi Guo, Ilgee Hong, Vicente Balmaseda, Changlong Yu, Liang Qiu, Xin Liu, Haoming Jiang, Tuo Zhao, Tianbao Yang |
| 2025 | Discriminative Policy Optimization for Token-Level Reward Models. Hongzhan Chen, Tao Yang, Shiping Gao, Ruijun Chen, Xiaojun Quan, Hongtao Tian, Ting Yao |
| 2025 | Disentangled Graph Spectral Domain Adaptation. Liang Yang, Xin Chen, Jiaming Zhuo, Di Jin, Chuan Wang, Xiaochun Cao, Zhen Wang, Yuanfang Guo |
| 2025 | Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts. Haoyang Li, Xin Wang, Xueling Zhu, Weigao Wen, Wenwu Zhu |
| 2025 | Disentangling and Integrating Relational and Sensory Information in Transformer Architectures. Awni Altabaa, John Lafferty |
| 2025 | Disparate Conditional Prediction in Multiclass Classifiers. Sivan Sabato, Eran Treister, Elad Yom-Tov |
| 2025 | Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms. Julius von Rohrscheidt, Bastian Rieck |
| 2025 | Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies. Yuefan Cao, Xiaoyu Li, Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Jiahao Zhang |
| 2025 | DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMs. Jongwoo Ko, Tianyi Chen, Sungnyun Kim, Tianyu Ding, Luming Liang, Ilya Zharkov, Se-Young Yun |
| 2025 | Distillation Scaling Laws. Dan Busbridge, Amitis Shidani, Floris Weers, Jason Ramapuram, Etai Littwin, Russell Webb |
| 2025 | Distillation of Discrete Diffusion through Dimensional Correlations. Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi, Hiromi Wakaki, Yuki Mitsufuji |
| 2025 | Distilling the Knowledge in Data Pruning. Emanuel Ben Baruch, Adam Botach, Igor Kviatkovsky, Manoj Aggarwal, Gérard G. Medioni |
| 2025 | Distinguishing Cause from Effect with Causal Velocity Models. Johnny Xi, Hugh Dance, Peter Orbanz, Benjamin Bloem-Reddy |
| 2025 | Distributed Conformal Prediction via Message Passing. Haifeng Wen, Hong Xing, Osvaldo Simeone |
| 2025 | Distributed Differentially Private Data Analytics via Secure Sketching. Jakob Burkhardt, Hannah Keller, Claudio Orlandi, Chris Schwiegelshohn |
| 2025 | Distributed Event-Based Learning via ADMM. Güner Dilsad Er, Sebastian Trimpe, Michael Muehlebach |
| 2025 | Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal. Deheng Yuan, Tao Guo, Zhongyi Huang |
| 2025 | Distributed Parallel Gradient Stacking(DPGS): Solving Whole Slide Image Stacking Challenge in Multi-Instance Learning. Boyuan Wu, Zefeng Wang, Xianwei Lin, Jiachun Xu, Jikai Yu, Shicheng Zhou, Hongda Chen, Lianxin Hu |
| 2025 | Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold. Yilong Song, Peijin Li, Bin Gao, Kun Yuan |
| 2025 | Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective. Yujin Oh, Pengfei Jin, Sangjoon Park, Sekeun Kim, Siyeop Yoon, Jin Sung Kim, Kyungsang Kim, Xiang Li, Quanzheng Li |
| 2025 | Distributional Diffusion Models with Scoring Rules. Valentin De Bortoli, Alexandre Galashov, J. Swaroop Guntupalli, Guangyao Zhou, Kevin Patrick Murphy, Arthur Gretton, Arnaud Doucet |
| 2025 | Distributionally Robust Active Learning for Gaussian Process Regression. Shion Takeno, Yoshito Okura, Yu Inatsu, Tatsuya Aoyama, Tomonari Tanaka, Satoshi Akahane, Hiroyuki Hanada, Noriaki Hashimoto, Taro Murayama, Hanju Lee, Shinya Kojima, Ichiro Takeuchi |
| 2025 | Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping. Guangyi Liu, Suzan Iloglu, Michael Caldara, Joseph W. Durham, Michael M. Zavlanos |
| 2025 | Distributionally Robust Policy Learning under Concept Drifts. Jingyuan Wang, Zhimei Ren, Ruohan Zhan, Zhengyuan Zhou |
| 2025 | Diverging Preferences: When do Annotators Disagree and do Models Know? Michael J. Q. Zhang, Zhilin Wang, Jena D. Hwang, Yi Dong, Olivier Delalleau, Yejin Choi, Eunsol Choi, Xiang Ren, Valentina Pyatkin |
| 2025 | Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift. Minh Nguyen Nhat To, Paul F. R. Wilson, Viet Nguyen, Mohamed Harmanani, Michael Cooper, Fahimeh Fooladgar, Purang Abolmaesumi, Parvin Mousavi, Rahul G. Krishnan |
| 2025 | Diversified Flow Matching with Translation Identifiability. Sagar Shrestha, Xiao Fu |
| 2025 | Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity. Zhenglin Wan, Xingrui Yu, David Mark Bossens, Yueming Lyu, Qing Guo, Flint Xiaofeng Fan, Yew-Soon Ong, Ivor W. Tsang |
| 2025 | Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization. Michael S. Yao, James C. Gee, Osbert Bastani |
| 2025 | Divide and Conquer: Exploring Language-centric Tree Reasoning for Video Question-Answering. Zhaohe Liao, Jiangtong Li, Siyu Sun, Qingyang Liu, Fengshun Xiao, Tianjiao Li, Qiang Zhang, Guang Chen, Li Niu, Changjun Jiang, Liqing Zhang |
| 2025 | Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning. Zican Hu, Wei Liu, Xiaoye Qu, Xiangyu Yue, Chunlin Chen, Zhi Wang, Yu Cheng |
| 2025 | Divide and Conquer: Learning Label Distribution with Subtasks. Haitao Wu, Weiwei Li, Xiuyi Jia |
| 2025 | Diving into Self-Evolving Training for Multimodal Reasoning. Wei Liu, Junlong Li, Xiwen Zhang, Fan Zhou, Yu Cheng, Junxian He |
| 2025 | Do Bayesian Neural Networks Actually Behave Like Bayesian Models? Gábor Pituk, Vik Shirvaikar, Tom Rainforth |
| 2025 | Do Multiple Instance Learning Models Transfer? Daniel Shao, Richard J. Chen, Andrew H. Song, Joel Runevic, Ming Y. Lu, Tong Ding, Faisal Mahmood |
| 2025 | Do NOT Think That Much for 2+3=? On the Overthinking of Long Reasoning Models. Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu |
| 2025 | Do Not Mimic My Voice : Speaker Identity Unlearning for Zero-Shot Text-to-Speech. Taesoo Kim, Jinju Kim, Dongchan Kim, Jong Hwan Ko, Gyeong-Moon Park |
| 2025 | Do Vision-Language Models Really Understand Visual Language? Yifan Hou, Buse Giledereli, Yilei Tu, Mrinmaya Sachan |
| 2025 | Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective. Zeyu Jia, Alexander Rakhlin, Tengyang Xie |
| 2025 | Do We Really Need Message Passing in Brain Network Modeling? Liang Yang, Yuwei Liu, Jiaming Zhuo, Di Jin, Chuan Wang, Zhen Wang, Xiaochun Cao |
| 2025 | DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning. Xiaolong Xu, Yibo Zhou, Haolong Xiang, Xiaoyong Li, Xuyun Zhang, Lianyong Qi, Wanchun Dou |
| 2025 | DocVXQA: Context-Aware Visual Explanations for Document Question Answering. Mohamed Ali Souibgui, Changkyu Choi, Andrey Barsky, Kangsoo Jung, Ernest Valveny, Dimosthenis Karatzas |
| 2025 | Does Data Scaling Lead to Visual Compositional Generalization? Arnas Uselis, Andrea Dittadi, Seong Joon Oh |
| 2025 | Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion. Kulin Shah, Alkis Kalavasis, Adam R. Klivans, Giannis Daras |
| 2025 | Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis. Qunzhong Wang, Xiangguo Sun, Hong Cheng |
| 2025 | Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks? Zi Liang, Haibo Hu, Qingqing Ye, Yaxin Xiao, Ronghua Li |
| 2025 | Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning. Hui Zeng, Wenke Huang, Tongqing Zhou, Xinyi Wu, Guancheng Wan, Yingwen Chen, Zhiping Cai |
| 2025 | Does learning the right latent variables necessarily improve in-context learning? Sarthak Mittal, Eric Elmoznino, Léo Gagnon, Sangnie Bhardwaj, Guillaume Lajoie, Dhanya Sridhar |
| 2025 | Domain-Adapted Diffusion Model for PROTAC Linker Design Through the Lens of Density Ratio in Chemical Space. Zixing Song, Ziqiao Meng, José Miguel Hernández-Lobato |
| 2025 | Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training. Mozhi Zhang, Howe Tissue, Lu Wang, Xipeng Qiu |
| 2025 | Don't Restart, Just Reuse: Reoptimizing MILPs with Dynamic Parameters. Sijia Zhang, Shuli Zeng, Shaoang Li, Feng Wu, Shaojie Tang, Xiangyang Li |
| 2025 | Double Machine Learning for Causal Inference under Shared-State Interference. Chris Hays, Manish Raghavan |
| 2025 | Double-Filter: Efficient Fine-tuning of Pre-trained Vision-Language Models via Patch&Layer Filtering. Yaoqin He, Junchen Fu, Kaiwen Zheng, Songpei Xu, Fuhai Chen, Jie Li, Joemon M. Jose, Xuri Ge |
| 2025 | Doubly Protected Estimation for Survival Outcomes Utilizing External Controls for Randomized Clinical Trials. Chenyin Gao, Shu Yang, Mingyang Shan, Wenyu Ye, Ilya Lipkovich, Douglas Faries |
| 2025 | Doubly Robust Conformalized Survival Analysis with Right-Censored Data. Matteo Sesia, Vladimir Svetnik |
| 2025 | Doubly Robust Fusion of Many Treatments for Policy Learning. Ke Zhu, Jianing Chu, Ilya Lipkovich, Wenyu Ye, Shu Yang |
| 2025 | DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion Model. Siwei Xia, Li Sun, Tiantian Sun, Qingli Li |
| 2025 | DragSolver: A Multi-Scale Transformer for Real-World Automotive Drag Coefficient Estimation. Ye Liu, Yuntian Chen |
| 2025 | DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization. Zhenglin Zhou, Xiaobo Xia, Fan Ma, Hehe Fan, Yi Yang, Tat-Seng Chua |
| 2025 | DriveGPT: Scaling Autoregressive Behavior Models for Driving. Xin Huang, Eric M. Wolff, Paul Vernaza, Tung Phan-Minh, Hongge Chen, David S. Hayden, Mark Edmonds, Brian Pierce, Xinxin Chen, Pratik Elias Jacob, Xiaobai Chen, Chingiz Tairbekov, Pratik Agarwal, Tianshi Gao, Yuning Chai, Siddhartha S. Srinivasa |
| 2025 | Drug-TTA: Test-Time Adaptation for Drug Virtual Screening via Multi-task Meta-Auxiliary Learning. Ao Shen, Mingzhi Yuan, Yingfan Ma, Jie Du, Qiao Huang, Manning Wang |
| 2025 | Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variant. Fabio Feser, Marina Evangelou |
| 2025 | Dueling Convex Optimization with General Preferences. Aadirupa Saha, Tomer Koren, Yishay Mansour |
| 2025 | DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination. Simin Chen, Pranav Pusarla, Baishakhi Ray |
| 2025 | DyPolySeg: Taylor Series-Inspired Dynamic Polynomial Fitting Network for Few-shot Point Cloud Semantic Segmentation. Changshuo Wang, Xiang Fang, Prayag Tiwari |
| 2025 | DynaMind: Reasoning over Abstract Video Dynamics for Embodied Decision-Making. Ziru Wang, Mengmeng Wang, Jade Dai, Teli Ma, Guo-Jun Qi, Yong Liu, Guang Dai, Jingdong Wang |
| 2025 | Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning. Chendi Ge, Xin Wang, Zeyang Zhang, Hong Chen, Jiapei Fan, Longtao Huang, Hui Xue, Wenwu Zhu |
| 2025 | Dynamic Similarity Graph Construction with Kernel Density Estimation. Steinar Laenen, Peter Macgregor, He Sun |
| 2025 | Dynamic Sparse Training of Diagonally Sparse Networks. Abhishek Tyagi, Arjun Iyer, William H. Renninger, Christopher Kanan, Yuhao Zhu |
| 2025 | Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data. Sayed Mohammad Hosseini, Maryam Shanechi |
| 2025 | Dynamical phases of short-term memory mechanisms in RNNs. Bariscan Kurtkaya, Fatih Dinc, Mert Yüksekgönül, Marta Blanco-Pozo, Ege Çirakman, Mark J. Schnitzer, Yucel Yemez, Hidenori Tanaka, Peng Yuan, Nina Miolane |
| 2025 | E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Time. Adam Breuer |
| 2025 | EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification. Zitong Shi, Guancheng Wan, Wenke Huang, Guibin Zhang, He Li, Carl Yang, Mang Ye |
| 2025 | EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization. Mujin Cheon, Jay H. Lee, Dong-Yeun Koh, Calvin Tsay |
| 2025 | EARTH: Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph. Guancheng Wan, Zewen Liu, Xiaojun Shan, Max S. Y. Lau, B. Aditya Prakash, Wei Jin |
| 2025 | EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping. Sam Gijsen, Kerstin Ritter |
| 2025 | EFDTR: Learnable Elliptical Fourier Descriptor Transformer for Instance Segmentation. Jiawei Cao, Chaochen Gu, Hao Cheng, Xiaofeng Zhang, Kaijie Wu, Changsheng Lu |
| 2025 | EGPlace: An Efficient Macro Placement Method via Evolutionary Search with Greedy Repositioning Guided Mutation. Ji Deng, Zhao Li, Ji Zhang, Jun Gao |
| 2025 | ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics. Letian Chen, Nina Marie Moorman, Matthew Craig Gombolay |
| 2025 | ELITE: Enhanced Language-Image Toxicity Evaluation for Safety. Wonjun Lee, Doehyeon Lee, Eugene Choi, Sangyoon Yu, Ashkan Yousefpour, Haon Park, Bumsub Ham, Suhyun Kim |
| 2025 | ELMO : Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces. Jinbin Zhang, Nasib Ullah, Erik Schultheis, Rohit Babbar |
| 2025 | ELoRA: Low-Rank Adaptation for Equivariant GNNs. Chen Wang, Siyu Hu, Guangming Tan, Weile Jia |
| 2025 | ENAHPool: The Edge-Node Attention-based Hierarchical Pooling for Graph Neural Networks. Zhehan Zhao, Lu Bai, Lixin Cui, Ming Li, Ziyu Lyu, Lixiang Xu, Yue Wang, Edwin R. Hancock |
| 2025 | ENSUR: Equitable and Statistically Unbiased Recommendation. Nitin Bisht, Xiuwen Gong, Guandong Xu |
| 2025 | EPIC: Efficient Position-Independent Caching for Serving Large Language Models. Junhao Hu, Wenrui Huang, Weidong Wang, Haoyi Wang, Tiancheng Hu, Qin Zhang, Hao Feng, Xusheng Chen, Yizhou Shan, Tao Xie |
| 2025 | EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling. Theodoros Kouzelis, Ioannis Kakogeorgiou, Spyros Gidaris, Nikos Komodakis |
| 2025 | ERICT: Enhancing Robustness by Identifying Concept Tokens in Zero-Shot Vision Language Models. Xinpeng Dong, Min Zhang, Didi Zhu, Ye Jun Jian, Keli Zhang, Aimin Zhou, Fei Wu, Kun Kuang |
| 2025 | ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport Plans. Ashkan Shahbazi, Elaheh Akbari, Darian Salehi, Xinran Liu, Navid Naderializadeh, Soheil Kolouri |
| 2025 | ETTA: Elucidating the Design Space of Text-to-Audio Models. Sang-gil Lee, Zhifeng Kong, Arushi Goel, Sungwon Kim, Rafael Valle, Bryan Catanzaro |
| 2025 | EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration. Allen Nie, Yi Su, Bo Chang, Jonathan Lee, Ed H. Chi, Quoc V. Le, Minmin Chen |
| 2025 | Earley-Driven Dynamic Pruning for Efficient Structured Decoding. Xintong Sun, Chi Wei, Minghao Tian, Shiwen Ni |
| 2025 | EasyInv: Toward Fast and Better DDIM Inversion. Ziyue Zhang, Mingbao Lin, Shuicheng Yan, Rongrong Ji |
| 2025 | EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM. Zhuofan Zong, Dongzhi Jiang, Bingqi Ma, Guanglu Song, Hao Shao, Dazhong Shen, Yu Liu, Hongsheng Li |
| 2025 | EcoMapper: Generative Modeling for Climate-Aware Satellite Imagery. Muhammed Goktepe, Amir Hossein Shamseddin, Erencan Uysal, Javier Muinelo Monteagudo, Lukas Drees, Aysim Toker, Senthold Asseng, Malte von Bloh |
| 2025 | Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges. Alex Crane, Thomas Stanley, Blair D. Sullivan, Nate Veldt |
| 2025 | EditLord: Learning Code Transformation Rules for Code Editing. Weichen Li, Albert Jan, Baishakhi Ray, Junfeng Yang, Chengzhi Mao, Kexin Pei |
| 2025 | Editable Concept Bottleneck Models. Lijie Hu, Chenyang Ren, Zhengyu Hu, Hongbin Lin, Cheng-Long Wang, Zhen Tan, Weimin Lyu, Jingfeng Zhang, Hui Xiong, Di Wang |
| 2025 | Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation. Mingyu Kang, Yong Suk Choi |
| 2025 | EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction. Ming Li, Yukang Cheng, Lu Bai, Feilong Cao, Ke Lv, Jiye Liang, Pietro Lio |
| 2025 | Effective and Efficient Masked Image Generation Models. Zebin You, Jingyang Ou, Xiaolu Zhang, Jun Hu, Jun Zhou, Chongxuan Li |
| 2025 | EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning. Dong Huang, Guangtao Zeng, Jianbo Dai, Meng Luo, Han Weng, Yuhao Qing, Heming Cui, Zhijiang Guo, Jie Zhang |
| 2025 | Efficient ANN-SNN Conversion with Error Compensation Learning. Chang Liu, Jiangrong Shen, Xuming Ran, Mingkun Xu, Qi Xu, Yi Xu, Gang Pan |
| 2025 | Efficient Bisection Projection to Ensure Neural-Network Solution Feasibility for Optimization over General Set. Enming Liang, Minghua Chen |
| 2025 | Efficient Core-set Selection for Deep Learning Through Squared Loss Minimization. Jianting Chen |
| 2025 | Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization. Youran Dong, Junfeng Yang, Wei Yao, Jin Zhang |
| 2025 | Efficient Diffusion Models for Symmetric Manifolds. Oren Mangoubi, Neil He, Nisheeth K. Vishnoi |
| 2025 | Efficient Distributed Optimization under Heavy-Tailed Noise. Su Hyeong Lee, Manzil Zaheer, Tian Li |
| 2025 | Efficient Federated Incomplete Multi-View Clustering. Suyuan Liu, Hao Yu, Hao Tan, Ke Liang, Siwei Wang, Shengju Yu, En Zhu, Xinwang Liu |
| 2025 | Efficient Fine-Grained Guidance for Diffusion Model Based Symbolic Music Generation. Tingyu Zhu, Haoyu Liu, Ziyu Wang, Zhimin Jiang, Zeyu Zheng |
| 2025 | Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance. Lisha Chen, Quan Xiao, Ellen Hidemi Fukuda, Xinyi Chen, Kun Yuan, Tianyi Chen |
| 2025 | Efficient Generative Modeling with Residual Vector Quantization-Based Tokens. Jaehyeon Kim, Taehong Moon, Keon Lee, Jaewoong Cho |
| 2025 | Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels-based Dataset Distillation. Rihong Qiu, Xinke Jiang, Yuchen Fang, Hongbin Lai, Hao Miao, Xu Chu, Junfeng Zhao, Yasha Wang |
| 2025 | Efficient Heterogeneity-Aware Federated Active Data Selection. Ying-Peng Tang, Chao Ren, Xiaoli Tang, Sheng-Jun Huang, Lizhen Cui, Han Yu |
| 2025 | Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling. Xiang Hu, Zhihao Teng, Jun Zhao, Wei Wu, Kewei Tu |
| 2025 | Efficient LiDAR Reflectance Compression via Scanning Serialization. Jiahao Zhu, Kang You, Dandan Ding, Zhan Ma |
| 2025 | Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment. Chengting Yu, Xiaochen Zhao, Lei Liu, Shu Yang, Gaoang Wang, Erping Li, Aili Wang |
| 2025 | Efficient Long Context Fine-tuning with Chunk Flow. Xiulong Yuan, Hongtao Xu, Wenting Shen, Ang Wang, Xiafei Qiu, Jie Zhang, Yuqiong Liu, Bowen Yu, Junyang Lin, Mingzhen Li, Weile Jia, Yong Li, Wei Lin |
| 2025 | Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow. Zhonglin Cao, Mario Geiger, Allan dos Santos Costa, Danny Reidenbach, Karsten Kreis, Tomas Geffner, Franco Pellegrini, Guoqing Zhou, Emine Küçükbenli |
| 2025 | Efficient Motion Prompt Learning for Robust Visual Tracking. Jie Zhao, Xin Chen, Yongsheng Yuan, Michael Felsberg, Dong Wang, Huchuan Lu |
| 2025 | Efficient Multi-modal Long Context Learning for Training-free Adaptation. Zehong Ma, Shiliang Zhang, Longhui Wei, Qi Tian |
| 2025 | Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination. Ilias Diakonikolas, Giannis Iakovidis, Daniel Kane, Thanasis Pittas |
| 2025 | Efficient Network Automatic Relevance Determination. Hongwei Zhang, Ziqi Ye, Xinyuan Wang, Xin Guo, Zenglin Xu, Yuan Cheng, Zixin Hu, Yuan Qi |
| 2025 | Efficient Noise Calculation in Deep Learning-based MRI Reconstructions. Onat Dalmaz, Arjun D. Desai, Reinhard Heckel, Tolga Çukur, Akshay S. Chaudhari, Brian A. Hargreaves |
| 2025 | Efficient Online Reinforcement Learning for Diffusion Policy. Haitong Ma, Tianyi Chen, Kai Wang, Na Li, Bo Dai |
| 2025 | Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method. Andi Han, Pierre-Louis Poirion, Akiko Takeda |
| 2025 | Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity. Wanjin Feng, Xingyu Gao, Wenqian Du, Hailong Shi, Peilin Zhao, Pengcheng Wu, Chunyan Miao |
| 2025 | Efficient Personalized Adaptation for Physiological Signal Foundation Model. Chenrui Wu, Haishuai Wang, Xiang Zhang, Chengqi Zhang, Jiajun Bu |
| 2025 | Efficient Quantification of Multimodal Interaction at Sample Level. Zequn Yang, Hongfa Wang, Di Hu |
| 2025 | Efficient Robotic Policy Learning via Latent Space Backward Planning. Dongxiu Liu, Haoyi Niu, Zhihao Wang, Jinliang Zheng, Yinan Zheng, Zhonghong Ou, Jianming Hu, Jianxiong Li, Xianyuan Zhan |
| 2025 | Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks. Thomas Massena, Léo Andéol, Thibaut Boissin, Franck Mamalet, Corentin Friedrich, Mathieu Serrurier, Sébastien Gerchinovitz |
| 2025 | Efficient Skill Discovery via Regret-Aware Optimization. He Zhang, Ming Zhou, Shaopeng Zhai, Ying Sun, Hui Xiong |
| 2025 | Efficient Source-free Unlearning via Energy-Guided Data Synthesis and Discrimination-Aware Multitask Optimization. Xiuyuan Wang, Chaochao Chen, Weiming Liu, Xinting Liao, Fan Wang, Xiaolin Zheng |
| 2025 | Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn |
| 2025 | Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs. Xun Wang, Jing Xu, Franziska Boenisch, Michael Backes, Christopher A. Choquette-Choo, Adam Dziedzic |
| 2025 | Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity. Erpai Luo, Xinran Wei, Lin Huang, Yunyang Li, Han Yang, Zaishuo Xia, Zun Wang, Chang Liu, Bin Shao, Jia Zhang |
| 2025 | Efficient and Separate Authentication Image Steganography Network. Junchao Zhou, Yao Lu, Jie Wen, Guangming Lu |
| 2025 | Efficiently Access Diffusion Fisher: Within the Outer Product Span Space. Fangyikang Wang, Hubery Yin, Shaobin Zhuang, Huminhao Zhu, Yinan Li, Lei Qian, Chao Zhang, Hanbin Zhao, Hui Qian, Chen Li |
| 2025 | Efficiently Serving Large Multimodal Models Using EPD Disaggregation. Gursimran Singh, Xinglu Wang, Yifan Hu, Timothy Tin Long Yu, Linzi Xing, Wei Jiang, Zhefeng Wang, Xiaolong Bai, Yi Li, Ying Xiong, Yong Zhang, Zhenan Fan |
| 2025 | Efficiently Vectorized MCMC on Modern Accelerators. Hugh Dance, Pierre Glaser, Peter Orbanz, Ryan P. Adams |
| 2025 | EgoPrivacy: What Your First-Person Camera Says About You? Yijiang Li, Genpei Zhang, Jiacheng Cheng, Yi Li, Xiaojun Shan, Dashan Gao, Jiancheng Lyu, Yuan Li, Ning Bi, Nuno Vasconcelos |
| 2025 | Ehrenfeucht-Haussler Rank and Chain of Thought. Pablo Barceló, Alexander Kozachinskiy, Tomasz Steifer |
| 2025 | Eigen Analysis of Conjugate Kernel and Neural Tangent Kernel. Xiangchao Li, Xiao Han, Qing Yang |
| 2025 | Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias. Yuanzhe Hu, Kinshuk Goel, Vlad Killiakov, Yaoqing Yang |
| 2025 | Eliciting Language Model Behaviors with Investigator Agents. Xiang Lisa Li, Neil Chowdhury, Daniel D. Johnson, Tatsunori Hashimoto, Percy Liang, Sarah Schwettmann, Jacob Steinhardt |
| 2025 | Elucidating Flow Matching ODE Dynamics via Data Geometry and Denoisers. Zhengchao Wan, Qingsong Wang, Gal Mishne, Yusu Wang |
| 2025 | Elucidating the Design Space of Multimodal Protein Language Models. Cheng-Yen Hsieh, Xinyou Wang, Daiheng Zhang, Dongyu Xue, Fei Ye, Shujian Huang, Zaixiang Zheng, Quanquan Gu |
| 2025 | Elucidating the design space of language models for image generation. Xuantong Liu, Shaozhe Hao, Xianbiao Qi, Tianyang Hu, Jun Wang, Rong Xiao, Yuan Yao |
| 2025 | Embedding Safety into RL: A New Take on Trust Region Methods. Nikola Milosevic, Johannes Müller, Nico Scherf |
| 2025 | EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents. Rui Yang, Hanyang Chen, Junyu Zhang, Mark Zhao, Cheng Qian, Kangrui Wang, Qineng Wang, Teja Venkat Koripella, Marziyeh Movahedi, Manling Li, Heng Ji, Huan Zhang, Tong Zhang |
| 2025 | Emergence and Effectiveness of Task Vectors in In-Context Learning: An Encoder Decoder Perspective. Seungwook Han, Jinyeop Song, Jeff Gore, Pulkit Agrawal |
| 2025 | Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. Neil Mallinar, Daniel Beaglehole, Libin Zhu, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin |
| 2025 | Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs. Jan Betley, Daniel Chee Hian Tan, Niels Warncke, Anna Sztyber-Betley, Xuchan Bao, Martín Soto, Nathan Labenz, Owain Evans |
| 2025 | Emergent Response Planning in LLMs. Zhichen Dong, Zhanhui Zhou, Zhixuan Liu, Chao Yang, Chaochao Lu |
| 2025 | Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models. Yukang Yang, Declan Campbell, Kaixuan Huang, Mengdi Wang, Jonathan D. Cohen, Taylor Whittington Webb |
| 2025 | EmoGrowth: Incremental Multi-label Emotion Decoding with Augmented Emotional Relation Graph. Kaicheng Fu, Changde Du, Jie Peng, Kunpeng Wang, Shuangchen Zhao, Xiaoyu Chen, Huiguang He |
| 2025 | Emoji Attack: Enhancing Jailbreak Attacks Against Judge LLM Detection. Zhipeng Wei, Yuqi Liu, N. Benjamin Erichson |
| 2025 | Emotional Face-to-Speech. Jiaxin Ye, Boyuan Cao, Hongming Shan |
| 2025 | Empirical Privacy Variance. Yuzheng Hu, Fan Wu, Ruicheng Xian, Yuhang Liu, Lydia Zakynthinou, Pritish Kamath, Chiyuan Zhang, David A. Forsyth |
| 2025 | Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks. Keyue Qiu, Yuxuan Song, Jie Yu, Hongbo Ma, Ziyao Cao, Zhilong Zhang, Yushuai Wu, Mingyue Zheng, Hao Zhou, Wei-Ying Ma |
| 2025 | Empowering World Models with Reflection for Embodied Video Prediction. Xiaowei Chi, Chun-Kai Fan, Hengyuan Zhang, Xingqun Qi, Rongyu Zhang, Anthony Chen, Chi-Min Chan, Wei Xue, Qifeng Liu, Shanghang Zhang, Yike Guo |
| 2025 | EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities. Talor Abramovich, Meet Udeshi, Minghao Shao, Kilian Lieret, Haoran Xi, Kimberly Milner, Sofija Jancheska, John Yang, Carlos E. Jimenez, Farshad Khorrami, Prashanth Krishnamurthy, Brendan Dolan-Gavitt, Muhammad Shafique, Karthik R. Narasimhan, Ramesh Karri, Ofir Press |
| 2025 | Enabling Optimal Decisions in Rehearsal Learning under CARE Condition. Wen-Bo Du, Hao-Yi Lei, Lue Tao, Tian-Zuo Wang, Zhi-Hua Zhou |
| 2025 | EncryptedLLM: Privacy-Preserving Large Language Model Inference via GPU-Accelerated Fully Homomorphic Encryption. Leo de Castro, Daniel Escudero, Adya Agrawal, Antigoni Polychroniadou, Manuela Veloso |
| 2025 | End-to-End Learning Framework for Solving Non-Markovian Optimal Control. Xiaole Zhang, Peiyu Zhang, Xiongye Xiao, Shixuan Li, Vasileios Tzoumas, Vijay Gupta, Paul Bogdan |
| 2025 | Energy-Based Flow Matching for Generating 3D Molecular Structure. Wenyin Zhou, Christopher Iliffe Sprague, Vsevolod Viliuga, Matteo Tadiello, Arne Elofsson, Hossein Azizpour |
| 2025 | Energy-Based Preference Model Offers Better Offline Alignment than the Bradley-Terry Preference Model. Yuzhong Hong, Hanshan Zhang, Junwei Bao, Hongfei Jiang, Yang Song |
| 2025 | Enforcing Idempotency in Neural Networks. Nikolaj Banke Jensen, Jamie Vicary |
| 2025 | Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation. Alessandro Palma, Sergei Rybakov, Leon Hetzel, Stephan Günnemann, Fabian J. Theis |
| 2025 | Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability. Jie Bao, Chuangyin Dang, Rui Luo, Hanwei Zhang, Zhixin Zhou |
| 2025 | Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss. Bo-Han Lai, Pin-Han Huang, Bo-Han Kung, Shang-Tse Chen |
| 2025 | Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration. Andreas Kontogiannis, Konstantinos Papathanasiou, Yi Shen, Giorgos Stamou, Michael M. Zavlanos, George A. Vouros |
| 2025 | Enhancing Decision-Making of Large Language Models via Actor-Critic. Heng Dong, Kefei Duan, Chongjie Zhang |
| 2025 | Enhancing Diversity In Parallel Agents: A Maximum State Entropy Exploration Story. Vincenzo De Paola, Riccardo Zamboni, Mirco Mutti, Marcello Restelli |
| 2025 | Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. Luca Masserano, Abdul Fatir Ansari, Boran Han, Xiyuan Zhang, Christos Faloutsos, Michael W. Mahoney, Andrew Gordon Wilson, Youngsuk Park, Syama Sundar Rangapuram, Danielle C. Maddix, Bernie Wang |
| 2025 | Enhancing Foundation Models with Federated Domain Knowledge Infusion. Jiaqi Wang, Jingtao Li, Weiming Zhuang, Chen Chen, Lingjuan Lyu, Fenglong Ma |
| 2025 | Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance. Yusong Wang, Shiyin Tan, Jialun Shen, Yicheng Xu, Haobo Song, Qi Xu, Prayag Tiwari, Mingkun Xu |
| 2025 | Enhancing Graph Invariant Learning from a Negative Inference Perspective. Kuo Yang, Zhengyang Zhou, Qihe Huang, Wenjie Du, Limin Li, Wu Jiang, Yang Wang |
| 2025 | Enhancing Ligand Validity and Affinity in Structure-Based Drug Design with Multi-Reward Optimization. Seungbeom Lee, Munsun Jo, Jungseul Ok, Dongwoo Kim |
| 2025 | Enhancing Logits Distillation with Plug&Play Kendall's τ Ranking Loss. Yuchen Guan, Runxi Cheng, Kang Liu, Chun Yuan |
| 2025 | Enhancing Parallelism in Decentralized Stochastic Convex Optimization. Ofri Eisen, Ron Dorfman, Kfir Yehuda Levy |
| 2025 | Enhancing Performance of Explainable AI Models with Constrained Concept Refinement. Geyu Liang, Senne Michielssen, Salar Fattahi |
| 2025 | Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models. Tung Minh Luu, Younghwan Lee, Donghoon Lee, Sunho Kim, Min Jun Kim, Chang D. Yoo |
| 2025 | Enhancing Spectral GNNs: From Topology and Perturbation Perspectives. Taoyang Qin, Ke-Jia Chen, Zheng Liu |
| 2025 | Enhancing Statistical Validity and Power in Hybrid Controlled Trials: A Randomization Inference Approach with Conformal Selective Borrowing. Ke Zhu, Shu Yang, Xiaofei Wang |
| 2025 | Enhancing Target-unspecific Tasks through a Features Matrix. Fangming Cui, Yonggang Zhang, Xuan Wang, Xinmei Tian, Jun Yu |
| 2025 | Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective. Hechuan Wen, Tong Chen, Mingming Gong, Li Kheng Chai, Shazia Sadiq, Hongzhi Yin |
| 2025 | Enhancing Visual Localization with Cross-Domain Image Generation. Yuanze Wang, Yichao Yan, Shiming Song, Songchang Jin, Yilan Huang, Xingdong Sheng, Dianxi Shi |
| 2025 | Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks. Jincheng Huang, Yujie Mo, Xiaoshuang Shi, Lei Feng, Xiaofeng Zhu |
| 2025 | EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification. Ben Dai |
| 2025 | Ensemble Distribution Distillation via Flow Matching. Jonggeon Park, Giung Nam, Hyunsu Kim, Jongmin Yoon, Juho Lee |
| 2025 | Ensemble Learned Bloom Filters: Two Oracles are Better than One. Ming Lin, Lin Chen |
| 2025 | EpiCoder: Encompassing Diversity and Complexity in Code Generation. Yaoxiang Wang, Haoling Li, Xin Zhang, Jie Wu, Xiao Liu, Wenxiang Hu, Zhongxin Guo, Yangyu Huang, Ying Xin, Yujiu Yang, Jinsong Su, Qi Chen, Scarlett Li |
| 2025 | Epsilon-VAE: Denoising as Visual Decoding. Long Zhao, Sanghyun Woo, Ziyu Wan, Yandong Li, Han Zhang, Boqing Gong, Hartwig Adam, Xuhui Jia, Ting Liu |
| 2025 | EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations. Haotian Zhai, Connor Lawless, Ellen Vitercik, Liu Leqi |
| 2025 | Equivalence is All: A Unified View for Self-supervised Graph Learning. Yejiang Wang, Yuhai Zhao, Zhengkui Wang, Ling Li, Jiapu Wang, Fangting Li, Miaomiao Huang, Shirui Pan, Xingwei Wang |
| 2025 | Equivariant Neural Tangent Kernels. Philipp Misof, Pan Kessel, Jan E. Gerken |
| 2025 | Equivariant Polynomial Functional Networks. Thieu Vo, Hoang V. Tran, Tho Tran Huu, An Nguyen The, Thanh Tran, Minh-Khoi Nguyen-Nhat, Duy-Tung Pham, Tan Minh Nguyen |
| 2025 | EraseAnything: Enabling Concept Erasure in Rectified Flow Transformers. Daiheng Gao, Shilin Lu, Wenbo Zhou, Jiaming Chu, Jie Zhang, Mengxi Jia, Bang Zhang, Zhaoxin Fan, Weiming Zhang |
| 2025 | Ergodic Generative Flows. Leo Maxime Brunswic, Mateo Clémente, Rui Heng Yang, Adam Sigal, Amir Rasouli, Yinchuan Li |
| 2025 | Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems. Maksim Zhdanov, Max Welling, Jan-Willem van de Meent |
| 2025 | EvFocus: Learning to Reconstruct Sharp Images from Out-of-Focus Event Streams. Lin Zhu, Xiantao Ma, Xiao Wang, Lizhi Wang, Hua Huang |
| 2025 | Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators. Yilun Zhou, Austin Xu, Peifeng Wang, Caiming Xiong, Shafiq Joty |
| 2025 | Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving. Yuxuan Zhou, Xien Liu, Chenwei Yan, Chen Ning, Xiao Zhang, Boxun Li, Xiangling Fu, Shijin Wang, Guoping Hu, Yu Wang, Ji Wu |
| 2025 | Evaluating Neuron Explanations: A Unified Framework with Sanity Checks. Tuomas P. Oikarinen, Ge Yan, Tsui-Wei Weng |
| 2025 | Event-Customized Image Generation. Zhen Wang, Yilei Jiang, Dong Zheng, Jun Xiao, Long Chen |
| 2025 | Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition. Zheyang Xiong, Ziyang Cai, John Cooper, Albert Ge, Vasilis Papageorgiou, Zack Sifakis, Angeliki Giannou, Ziqian Lin, Liu Yang, Saurabh Agarwal, Grigorios Chrysos, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos |
| 2025 | EvoControl: Multi-Frequency Bi-Level Control for High-Frequency Continuous Control. Samuel Holt, Todor Davchev, Dhruva Tirumala, Ben Moran, Atil Iscen, Antoine Laurens, Yixin Lin, Erik Frey, Markus Wulfmeier, Francesco Romano, Nicolas Heess |
| 2025 | EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions. Huayu Deng, Xiangming Zhu, Yunbo Wang, Xiaokang Yang |
| 2025 | EvoPress: Accurate Dynamic Model Compression via Evolutionary Search. Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh |
| 2025 | Evolving Minds: Logic-Informed Inference from Temporal Action Patterns. Chao Yang, Shuting Cui, Yang Yang, Shuang Li |
| 2025 | Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective. Jianyu Wang, Zhiqiang Hu, Lidong Bing |
| 2025 | Ex-VAD: Explainable Fine-grained Video Anomaly Detection Based on Visual-Language Models. Chao Huang, Yushu Shi, Jie Wen, Wei Wang, Yong Xu, Xiaochun Cao |
| 2025 | ExLM: Rethinking the Impact of [MASK] Tokens in Masked Language Models. Kangjie Zheng, Junwei Yang, Siyue Liang, Bin Feng, Zequn Liu, Wei Ju, Zhiping Xiao, Ming Zhang |
| 2025 | ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts. Samar Khanna, Medhanie Irgau, David B. Lobell, Stefano Ermon |
| 2025 | Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements. Arya Mazumdar, Neha Sangwan |
| 2025 | Exact Upper and Lower Bounds for the Output Distribution of Neural Networks with Random Inputs. Andrey Kofnov, Daniel Kapla, Ezio Bartocci, Efstathia Bura |
| 2025 | Exact risk curves of signSGD in High-Dimensions: quantifying preconditioning and noise-compression effects. Ke Liang Xiao, Noah Marshall, Atish Agarwala, Elliot Paquette |
| 2025 | Exactly Tight Information-theoretic Generalization Bounds via Binary Jensen-Shannon Divergence. Yuxin Dong, Haoran Guo, Tieliang Gong, Wen Wen, Chen Li |
| 2025 | Exogenous Isomorphism for Counterfactual Identifiability. Yikang Chen, Dehui Du |
| 2025 | ExpProof : Operationalizing Explanations for Confidential Models with ZKPs. Chhavi Yadav, Evan Laufer, Dan Boneh, Kamalika Chaudhuri |
| 2025 | Expected Variational Inequalities. Brian Hu Zhang, Ioannis Anagnostides, Emanuel Tewolde, Ratip Emin Berker, Gabriele Farina, Vincent Conitzer, Tuomas Sandholm |
| 2025 | Expert Race: A Flexible Routing Strategy for Scaling Diffusion Transformer with Mixture of Experts. Yike Yuan, Ziyu Wang, Zihao Huang, Defa Zhu, Xun Zhou, Jingyi Yu, Qiyang Min |
| 2025 | Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations. Aditya Taparia, Som Sagar, Ransalu Senanayake |
| 2025 | Explaining the role of Intrinsic Dimensionality in Adversarial Training. Enes Altinisik, Safa Messaoud, Husrev Taha Sencar, Hassan Sajjad, Sanjay Chawla |
| 2025 | Explaining, Fast and Slow: Abstraction and Refinement of Provable Explanations. Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner, Matthias Althoff, Guy Katz |
| 2025 | Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization. Yang Shen, Xiu-Shen Wei, Yifan Sun, Yuxin Song, Tao Yuan, Jian Jin, He-Yang Xu, Yazhou Yao, Errui Ding |
| 2025 | Explicit Discovery of Nonlinear Symmetries from Dynamic Data. Lexiang Hu, Yikang Li, Zhouchen Lin |
| 2025 | Explicit Exploration for High-Welfare Equilibria in Game-Theoretic Multiagent Reinforcement Learning. Austin A. Nguyen, Anri Gu, Michael P. Wellman |
| 2025 | Explicit Preference Optimization: No Need for an Implicit Reward Model. Xiangkun Hu, Lemin Kong, Tong He, David Wipf |
| 2025 | Exploiting Curvature in Online Convex Optimization with Delayed Feedback. Hao Qiu, Emmanuel Esposito, Mengxiao Zhang |
| 2025 | Exploiting Presentative Feature Distributions for Parameter-Efficient Continual Learning of Large Language Models. Xin Cheng, Jiabo Ye, Haiyang Xu, Ming Yan, Ji Zhang, Feng Liu, Fei Huang, Lei Feng |
| 2025 | Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization. Yuki Takezawa, Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich |
| 2025 | Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning. Puning Yang, Qizhou Wang, Zhuo Huang, Tongliang Liu, Chengqi Zhang, Bo Han |
| 2025 | Exploring Invariance in Images through One-way Wave Equations. Yinpeng Chen, Dongdong Chen, Xiyang Dai, Mengchen Liu, Yinan Feng, Youzuo Lin, Lu Yuan, Zicheng Liu |
| 2025 | Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling. Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin, Théo Bontempelli, Thomas Bouabça, Tristan Cazenave |
| 2025 | Exploring Representations and Interventions in Time Series Foundation Models. Michal Wilinski, Mononito Goswami, Willa Potosnak, Nina Zukowska, Artur Dubrawski |
| 2025 | Exploring Vision Semantic Prompt for Efficient Point Cloud Understanding. Yixin Zha, Chuxin Wang, Wenfei Yang, Tianzhu Zhang, Feng Wu |
| 2025 | Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards. Yangsibo Huang, Milad Nasr, Anastasios Nikolas Angelopoulos, Nicholas Carlini, Wei-Lin Chiang, Christopher A. Choquette-Choo, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Ken Liu, Ion Stoica, Florian Tramèr, Chiyuan Zhang |
| 2025 | Exponential Family Variational Flow Matching for Tabular Data Generation. Andrés Guzmán-Cordero, Floor Eijkelboom, Jan-Willem van de Meent |
| 2025 | Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs. Ziang Chen, Xiaohan Chen, Jialin Liu, Xinshang Wang, Wotao Yin |
| 2025 | Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization. Ziyu Gong, Jim Lim, David I. Inouye |
| 2025 | ExtPose: Robust and Coherent Pose Estimation by Extending ViTs. Rongyu Chen, Li'an Zhuo, Linlin Yang, Qi Wang, Liefeng Bo, Bang Zhang, Angela Yao |
| 2025 | Extracting Rare Dependence Patterns via Adaptive Sample Reweighting. Yiqing Li, Yewei Xia, Xiaofei Wang, Zhengming Chen, Liuhua Peng, Mingming Gong, Kun Zhang |
| 2025 | Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts. Jiahai Feng, Stuart Russell, Jacob Steinhardt |
| 2025 | Extreme Value Policy Optimization for Safe Reinforcement Learning. Shiqing Gao, Yihang Zhou, Shuai Shao, Haoyu Luo, Yiheng Bing, Jiaxin Ding, Luoyi Fu, Xinbing Wang |
| 2025 | FAB-PPI: Frequentist, Assisted by Bayes, Prediction-Powered Inference. Stefano Cortinovis, Francois Caron |
| 2025 | FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems. Arya Fayyazi, Mehdi Kamal, Massoud Pedram |
| 2025 | FDGen: A Fairness-Aware Graph Generation Model. Zichong Wang, Wenbin Zhang |
| 2025 | FEAT-KD: Learning Concise Representations for Single and Multi-Target Regression via TabNet Knowledge Distillation. Kei Sen Fong, Mehul Motani |
| 2025 | FG-CLIP: Fine-Grained Visual and Textual Alignment. Chunyu Xie, Bin Wang, Fanjing Kong, Jincheng Li, Dawei Liang, Gengshen Zhang, Dawei Leng, Yuhui Yin |
| 2025 | FIC-TSC: Learning Time Series Classification with Fisher Information Constraint. Xiwen Chen, Wenhui Zhu, Peijie Qiu, Hao Wang, Huayu Li, Zihan Li, Yalin Wang, Aristeidis Sotiras, Abolfazl Razi |
| 2025 | FLAM: Frame-Wise Language-Audio Modeling. Yusong Wu, Christos Tsirigotis, Ke Chen, Cheng-Zhi Anna Huang, Aaron C. Courville, Oriol Nieto, Prem Seetharaman, Justin Salamon |
| 2025 | FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models. Xinting Liao, Weiming Liu, Jiaming Qian, Pengyang Zhou, Jiahe Xu, Wenjie Wang, Chaochao Chen, Xiaolin Zheng, Tat-Seng Chua |
| 2025 | FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making. Yucen Wang, Rui Yu, Shenghua Wan, Le Gan, De-Chuan Zhan |
| 2025 | FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training. Philip Zmushko, Aleksandr Beznosikov, Martin Takác, Samuel Horváth |
| 2025 | FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation. Srijith Nair, Michael Lin, Peizhong Ju, Amirreza Talebi, Elizabeth Serena Bentley, Jia Liu |
| 2025 | FSTLLM: Spatio-Temporal LLM for Few Shot Time Series Forecasting. Yue Jiang, Yile Chen, Xiucheng Li, Qin Chao, Shuai Liu, Gao Cong |
| 2025 | FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees. Fan Nie, Xiaotian Hou, Shuhang Lin, James Zou, Huaxiu Yao, Linjun Zhang |
| 2025 | Fair Clustering via Alignment. Kunwoong Kim, Jihu Lee, Sangchul Park, Yongdai Kim |
| 2025 | FairICP: Encouraging Equalized Odds via Inverse Conditional Permutation. Yuheng Lai, Leying Guan |
| 2025 | FairPFN: A Tabular Foundation Model for Causal Fairness. Jake Robertson, Noah Hollmann, Samuel Müller, Noor H. Awad, Frank Hutter |
| 2025 | Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective. Firas Laakom, Haobo Chen, Jürgen Schmidhuber, Yuheng Bu |
| 2025 | Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness. Haoxuan Li, Zeyu Tang, Zhichao Jiang, Zhuangyan Fang, Yue Liu, Zhi Geng, Kun Zhang |
| 2025 | Falcon: Fast Visuomotor Policies via Partial Denoising. Haojun Chen, Minghao Liu, Chengdong Ma, Xiaojian Ma, Zailin Ma, Huimin Wu, Yuanpei Chen, Yifan Zhong, Mingzhi Wang, Qing Li, Yaodong Yang |
| 2025 | False Coverage Proportion Control for Conformal Prediction. Alexandre Blain, Bertrand Thirion, Pierre Neuvial |
| 2025 | Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms. Rickard Karlsson, Jesse H. Krijthe |
| 2025 | Fast Estimation of Partial Dependence Functions using Trees. Jinyang Liu, Tessa Steensgaard, Marvin N. Wright, Niklas Pfister, Munir Hiabu |
| 2025 | Fast Exact Unlearning for In-Context Learning Data for LLMs. Andrei Ioan Muresanu, Anvith Thudi, Michael R. Zhang, Nicolas Papernot |
| 2025 | Fast Incomplete Multi-view Clustering by Flexible Anchor Learning. Yalan Qin, Guorui Feng, Xinpeng Zhang |
| 2025 | Fast Inference with Kronecker-Sparse Matrices. Antoine Gonon, Léon Zheng, Pascal Carrivain, Quoc-Tung Le |
| 2025 | Fast Large Language Model Collaborative Decoding via Speculation. Jiale Fu, Yuchu Jiang, Junkai Chen, Jiaming Fan, Xin Geng, Xu Yang |
| 2025 | Fast Min-ϵ Segmented Regression using Constant-Time Segment Merging. Ansgar Lößer, Max Schlecht, Florian Schintke, Joel Witzke, Matthias Weidlich, Björn Scheuermann |
| 2025 | Fast Tensor Completion via Approximate Richardson Iteration. Mehrdad Ghadiri, Matthew Fahrbach, Yunbum Kook, Ali Jadbabaie |
| 2025 | Fast Video Generation with Sliding Tile Attention. Peiyuan Zhang, Yongqi Chen, Runlong Su, Hangliang Ding, Ion Stoica, Zhengzhong Liu, Hao Zhang |
| 2025 | Fast and Low-Cost Genomic Foundation Models via Outlier Removal. Haozheng Luo, Chenghao Qiu, Maojiang Su, Zhihan Zhou, Zoe Mehta, Guo Ye, Jerry Yao-Chieh Hu, Han Liu |
| 2025 | Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity. Jian-Feng Cai, Zhuozhi Xian, Jiaxi Ying |
| 2025 | Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments. Yun Qu, Cheems Wang, Yixiu Mao, Yiqin Lv, Xiangyang Ji |
| 2025 | Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization. Shiyu Wang, Mariam Avagyan, Yihan Shen, Arnaud Lamy, Tingran Wang, Szabolcs Márka, Zsuzsa Márka, John Wright |
| 2025 | FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks. Laines Schmalwasser, Niklas Penzel, Joachim Denzler, Julia Niebling |
| 2025 | Faster Approximation Algorithms for k-Center via Data Reduction. Arnold Filtser, Shaofeng H.-C. Jiang, Yi Li, Anurag Murty Naredla, Ioannis Psarros, Qiaoyuan Yang, Qin Zhang |
| 2025 | Faster Global Minimum Cut with Predictions. Helia Niaparast, Benjamin Moseley, Karan Singh |
| 2025 | Faster Rates for Private Adversarial Bandits. Hilal Asi, Vinod Raman, Kunal Talwar |
| 2025 | Faster Stochastic Optimization with Arbitrary Delays via Adaptive Asynchronous Mini-Batching. Amit Attia, Ofir Gaash, Tomer Koren |
| 2025 | Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation. Zecheng Hao, Qichao Ma, Kang Chen, Yi Zhang, Zhaofei Yu, Tiejun Huang |
| 2025 | Feasible Action Search for Bandit Linear Programs via Thompson Sampling. Aditya Gangrade, Aldo Pacchiano, Clayton Scott, Venkatesh Saligrama |
| 2025 | FeatSharp: Your Vision Model Features, Sharper. Mike Ranzinger, Greg Heinrich, Pavlo Molchanov, Bryan Catanzaro, Andrew Tao |
| 2025 | Feature Importance Metrics in the Presence of Missing Data. Henrik von Kleist, Joshua Wendland, Ilya Shpitser, Carsten Marr |
| 2025 | Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry. Chi-Ning Chou, Hang Le, Yichen Wang, SueYeon Chung |
| 2025 | Feature Shift Localization Network. Míriam Barrabés, Daniel Mas Montserrat, Kapal Dev, Alexander G. Ioannidis |
| 2025 | Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions. Fabiola Ricci, Lorenzo Bardone, Sebastian Goldt |
| 2025 | Feature out! Let Raw Image as Your Condition for Blind Face Restoration. Xinmin Qiu, Gege Chen, Bonan Li, Congying Han, Tiande Guo, Zicheng Zhang |
| 2025 | Feature-Mapping Topology Optimization with Neural Heaviside Signed Distance Functions. Aleksandr Kolomeitsev, Anh Huy Phan |
| 2025 | Features are fate: a theory of transfer learning in high-dimensional regression. Javan Tahir, Surya Ganguli, Grant M. Rotskoff |
| 2025 | FedBEns: One-Shot Federated Learning based on Bayesian Ensemble. Jacopo Talpini, Marco Savi, Giovanni Neglia |
| 2025 | FedClean: A General Robust Label Noise Correction for Federated Learning. Xiaoqian Jiang, Jing Zhang |
| 2025 | FedECADO: A Dynamical System Model of Federated Learning. Aayushya Agarwal, Gauri Joshi, Lawrence T. Pileggi |
| 2025 | FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning. Ganyu Wang, Jinjie Fang, Maxwell J. Yin, Bin Gu, Xi Chen, Boyu Wang, Yi Chang, Charles Ling |
| 2025 | FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation. Chengying Fang, Wenke Huang, Guancheng Wan, Yihao Yang, Mang Ye |
| 2025 | FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates. Xinyi Lu, Hao Zhang, Chenglin Li, Weijia Lu, Zhifei Yang, Wenrui Dai, Xiaodong Zhang, Xiaofeng Ma, Can Zhang, Junni Zou, Hongkai Xiong |
| 2025 | FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence. Yichen Li, Yuying Wang, Haozhao Wang, Yining Qi, Tianzhe Xiao, Ruixuan Li |
| 2025 | Federated Causal Structure Learning with Non-identical Variable Sets. Yunxia Wang, Fuyuan Cao, Kui Yu, Jiye Liang |
| 2025 | Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation. Yihao Yang, Wenke Huang, Guancheng Wan, Bin Yang, Mang Ye |
| 2025 | Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework. Terje Mildner, Oliver Hamelijnck, Paris Giampouras, Theodoros Damoulas |
| 2025 | Federated In-Context Learning: Iterative Refinement for Improved Answer Quality. Ruhan Wang, Zhiyong Wang, Chengkai Huang, Rui Wang, Tong Yu, Lina Yao, John C. S. Lui, Dongruo Zhou |
| 2025 | Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance. Guoqing Chao, Zhenghao Zhang, Lei Meng, Jie Wen, Dianhui Chu |
| 2025 | Federated Learning for Feature Generalization with Convex Constraints. Dongwon Kim, Donghee Kim, Sung Kuk Shyn, Kwangsu Kim |
| 2025 | Federated Node-Level Clustering Network with Cross-Subgraph Link Mending. Jingxin Liu, Renda Han, Wenxuan Tu, Haotian Wang, Junlong Wu, Jieren Cheng |
| 2025 | Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework. Guan Huang, Tao Shu |
| 2025 | Feedforward Few-shot Species Range Estimation. Christian Lange, Max Hamilton, Elijah Cole, Alexander Shepard, Samuel Heinrich, Angela Zhu, Subhransu Maji, Grant Van Horn, Oisin Mac Aodha |
| 2025 | Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. Yao Shu, Wenyang Hu, See-Kiong Ng, Bryan Kian Hsiang Low, Fei Richard Yu |
| 2025 | Few-Shot Learner Generalizes Across AI-Generated Image Detection. Shiyu Wu, Jing Liu, Jing Li, Yequan Wang |
| 2025 | Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts. Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alán Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov |
| 2025 | FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks. Zhaoxuan Kan, Husheng Han, Shangyi Shi, Tenghui Hua, Hang Lu, Xiaowei Li, Jianan Mu, Xing Hu |
| 2025 | Field Matching: an Electrostatic Paradigm to Generate and Transfer Data. Alexander Kolesov, S. I. Manukhov, Vladimir Vladimirovich Palyulin, Alexander Korotin |
| 2025 | Finding Wasserstein Ball Center: Efficient Algorithm and The Applications in Fairness. Yuntao Wang, Yuxuan Li, Qingyuan Yang, Hu Ding |
| 2025 | Fine-Grained Captioning of Long Videos through Scene Graph Consolidation. Sanghyeok Chu, Seonguk Seo, Bohyung Han |
| 2025 | Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean Field Games. Antonio Ocello, Daniil Tiapkin, Lorenzo Mancini, Mathieu Laurière, Eric Moulines |
| 2025 | Finite-Time Analysis of Discrete-Time Stochastic Interpolants. Yuhao Liu, Yu Chen, Rui Hu, Longbo Huang |
| 2025 | Finite-Time Convergence Rates in Stochastic Stackelberg Games with Smooth Algorithmic Agents. Eric Frankel, Kshitij Kulkarni, Dmitriy Drusvyatskiy, Sewoong Oh, Lillian J. Ratliff |
| 2025 | Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning. Zhiyao Zhang, Myeung Suk Oh, Hairi, Ziyue Luo, Alvaro Velasquez, Jia Liu |
| 2025 | FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing. Yingying Deng, Xiangyu He, Changwang Mei, Peisong Wang, Fan Tang |
| 2025 | FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain. Rohan Deb, Kiran Koshy Thekumparampil, Kousha Kalantari, Gaurush Hiranandani, Shoham Sabach, Branislav Kveton |
| 2025 | Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator. Yu Xin Li, Felix Dangel, Derek Tam, Colin Raffel |
| 2025 | Fixed-Confidence Multiple Change Point Identification under Bandit Feedback. Joseph Lazzaro, Ciara Pike-Burke |
| 2025 | Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function. Christopher Subich, Syed Zahid Husain, Leo Separovic, Jing Yang |
| 2025 | Fixing the Loose Brake: Exponential-Tailed Stopping Time in Best Arm Identification. Kapilan Balagopalan, Tuan Ngo Nguyen, Yao Zhao, Kwang-Sung Jun |
| 2025 | FlashTP: Fused, Sparsity-Aware Tensor Product for Machine Learning Interatomic Potentials. Seung Yul Lee, Hojoon Kim, Yutack Park, Dawoon Jeong, Seungwu Han, Yeonhong Park, Jae W. Lee |
| 2025 | Flat-LoRA: Low-Rank Adaptation over a Flat Loss Landscape. Tao Li, Zhengbao He, Yujun Li, Yasheng Wang, Lifeng Shang, Xiaolin Huang |
| 2025 | FlatQuant: Flatness Matters for LLM Quantization. Yuxuan Sun, Ruikang Liu, Haoli Bai, Han Bao, Kang Zhao, Yuening Li, Jiaxin Hu, Xianzhi Yu, Lu Hou, Chun Yuan, Xin Jiang, Wulong Liu, Jun Yao |
| 2025 | Fleet of Agents: Coordinated Problem Solving with Large Language Models. Lars Henning Klein, Nearchos Potamitis, Roland C. Aydin, Robert West, Caglar Gulcehre, Akhil Arora |
| 2025 | Flex3D: Feed-Forward 3D Generation with Flexible Reconstruction Model and Input View Curation. Junlin Han, Jianyuan Wang, Andrea Vedaldi, Philip Torr, Filippos Kokkinos |
| 2025 | FlexControl: Computation-Aware Conditional Control with Differentiable Router for Text-to-Image Generation. Zheng Fang, Lichuan Xiang, Xu Cai, Kaicheng Zhou, Hongkai Wen |
| 2025 | FlexTok: Resampling Images into 1D Token Sequences of Flexible Length. Roman Bachmann, Jesse Allardice, David Mizrahi, Enrico Fini, Oguzhan Fatih Kar, Elmira Amirloo, Alaaeldin El-Nouby, Amir Zamir, Afshin Dehghan |
| 2025 | FlexiClip: Locality-Preserving Free-Form Character Animation. Anant Khandelwal |
| 2025 | FlexiReID: Adaptive Mixture of Expert for Multi-Modal Person Re-Identification. Zhen Sun, Lei Tan, Yunhang Shen, Chengmao Cai, Xing Sun, Pingyang Dai, Liujuan Cao, Rongrong Ji |
| 2025 | Flexibility-conditioned protein structure design with flow matching. Vsevolod Viliuga, Leif Seute, Nicolas Wolf, Simon Wagner, Arne Elofsson, Jan Stühmer, Frauke Gräter |
| 2025 | Flexible Tails for Normalizing Flows. Tennessee Hickling, Dennis Prangle |
| 2025 | Flexible and Efficient Grammar-Constrained Decoding. Kanghee Park, Timothy Zhou, Loris D'Antoni |
| 2025 | Flexible, Efficient, and Stable Adversarial Attacks on Machine Unlearning. Zihan Zhou, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Da Yan, Ruoming Jin, Dejing Dou |
| 2025 | FlipAttack: Jailbreak LLMs via Flipping. Yue Liu, Xiaoxin He, Miao Xiong, Jinlan Fu, Shumin Deng, Yingwei Ma, Jiaheng Zhang, Bryan Hooi |
| 2025 | FloE: On-the-Fly MoE Inference on Memory-constrained GPU. Yuxin Zhou, Zheng Li, Jun Zhang, Jue Wang, Yiping Wang, Zhongle Xie, Ke Chen, Lidan Shou |
| 2025 | Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions. Geonho Hwang, Yeachan Park, Wonyeol Lee, Sejun Park |
| 2025 | Flopping for FLOPs: Leveraging Equivariance for Computational Efficiency. Georg Bökman, David Nordström, Fredrik Kahl |
| 2025 | Flow Matching for Denoised Social Recommendation. Yinxuan Huang, Ke Liang, Zhuofan Dong, Xiaodong Qu, Tianxiang Wang, Yue Han, Jingao Xu, Bin Zhou, Ye Wang |
| 2025 | Flow Matching for Few-Trial Neural Adaptation with Stable Latent Dynamics. Puli Wang, Yu Qi, Yueming Wang, Gang Pan |
| 2025 | Flow Q-Learning. Seohong Park, Qiyang Li, Sergey Levine |
| 2025 | Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples. Fangxu Yu, Lai Jiang, Haoqiang Kang, Shibo Hao, Lianhui Qin |
| 2025 | Flow-based Domain Randomization for Learning and Sequencing Robotic Skills. Aidan Curtis, Eric Li, Michael Noseworthy, Nishad Gothoskar, Sachin Chitta, Hui Li, Leslie Pack Kaelbling, Nicole E. Carey |
| 2025 | Flow-field inference from neural data using deep recurrent networks. Timothy Doyeon Kim, Thomas Zhihao Luo, Tankut Can, Kamesh Krishnamurthy, Jonathan W. Pillow, Carlos D. Brody |
| 2025 | Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options. Lakshmi Nair, Ian Trase, J. Mark Kim |
| 2025 | FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching. Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan L. Yuille, Liang-Chieh Chen |
| 2025 | FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields. Gwanhyeong Koo, Sunjae Yoon, Younghwan Lee, Ji Woo Hong, Chang D. Yoo |
| 2025 | Flowing Datasets with Wasserstein over Wasserstein Gradient Flows. Clément Bonet, Christophe Vauthier, Anna Korba |
| 2025 | Fluctuations of the largest eigenvalues of transformed spiked Wigner matrices. Aro Lee, Ji Oon Lee |
| 2025 | Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification. Sicong Li, Qianqian Xu, Zhiyong Yang, Zitai Wang, Linchao Zhang, Xiaochun Cao, Qingming Huang |
| 2025 | Focus On This, Not That! Steering LLMs with Adaptive Feature Specification. Tom A. Lamb, Adam Davies, Alasdair Paren, Philip Torr, Francesco Pinto |
| 2025 | Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning. Zhenni Bi, Kai Han, Chuanjian Liu, Yehui Tang, Yunhe Wang |
| 2025 | Forty-second International Conference on Machine Learning, ICML 2025, Vancouver, BC, Canada, July 13-19, 2025 Aarti Singh, Maryam Fazel, Daniel Hsu, Simon Lacoste-Julien, Felix Berkenkamp, Tegan Maharaj, Kiri Wagstaff, Jerry Zhu |
| 2025 | Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection. Zhijing Wan, Zhixiang Wang, Zheng Wang, Xin Xu, Shin'ichi Satoh |
| 2025 | Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages. Michael Sun, Weize Yuan, Gang Liu, Wojciech Matusik, Jie Chen |
| 2025 | Fourier Position Embedding: Enhancing Attention's Periodic Extension for Length Generalization. Ermo Hua, Che Jiang, Xingtai Lv, Kaiyan Zhang, Youbang Sun, Yuchen Fan, Xuekai Zhu, Biqing Qi, Ning Ding, Bowen Zhou |
| 2025 | FourierMamba: Fourier Learning Integration with State Space Models for Image Deraining. Dong Li, Yidi Liu, Xueyang Fu, Jie Huang, Senyan Xu, Qi Zhu, Zheng-Jun Zha |
| 2025 | Fragments to Facts: Partial-Information Fragment Inference from LLMs. Lucas Rosenblatt, Bin Han, Robert Wolfe, Bill Howe |
| 2025 | FrameBridge: Improving Image-to-Video Generation with Bridge Models. Yuji Wang, Zehua Chen, Xiaoyu Chen, Yixiang Wei, Jun Zhu, Jianfei Chen |
| 2025 | Fraud-Proof Revenue Division on Subscription Platforms. Abheek Ghosh, Tzeh Yuan Neoh, Nicholas Teh, Giannis Tyrovolas |
| 2025 | Free Process Rewards without Process Labels. Lifan Yuan, Wendi Li, Huayu Chen, Ganqu Cui, Ning Ding, Kaiyan Zhang, Bowen Zhou, Zhiyuan Liu, Hao Peng |
| 2025 | FreeMesh: Boosting Mesh Generation with Coordinates Merging. Jian Liu, Haohan Weng, Biwen Lei, Xianghui Yang, Zibo Zhao, Zhuo Chen, Song Guo, Tao Han, Chunchao Guo |
| 2025 | Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM. Xiong Wang, Yangze Li, Chaoyou Fu, Yike Zhang, Yunhang Shen, Lei Xie, Ke Li, Xing Sun, Long Ma |
| 2025 | From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models. Xinyang Li, Siqi Liu, Bochao Zou, Jiansheng Chen, Huimin Ma |
| 2025 | From Complex to Atomic: Enhancing Augmented Generation via Knowledge-Aware Dual Rewriting and Reasoning. Jinyu Wang, Jingjing Fu, Rui Wang, Lei Song, Jiang Bian |
| 2025 | From Crowdsourced Data to High-quality Benchmarks: Arena-Hard and Benchbuilder Pipeline. Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Tianhao Wu, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica |
| 2025 | From Debate to Equilibrium: Belief‑Driven Multi‑Agent LLM Reasoning via Bayesian Nash Equilibrium. Xie Yi, Zhanke Zhou, Chentao Cao, Qiyu Niu, Tongliang Liu, Bo Han |
| 2025 | From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models. Mingjia Yin, Junwei Pan, Hao Wang, Ximei Wang, Shangyu Zhang, Jie Jiang, Defu Lian, Enhong Chen |
| 2025 | From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms. Jessica Dai, Paula Gradu, Inioluwa Deborah Raji, Benjamin Recht |
| 2025 | From Jack of All Trades to Master of One: Specializing LLM-based Autoraters to a Test Set. Mara Finkelstein, Daniel Deutsch, Parker Riley, Juraj Juraska, Geza Kovacs, Markus Freitag |
| 2025 | From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning. Noa Rubin, Kirsten Fischer, Javed Lindner, Inbar Seroussi, Zohar Ringel, Michael Krämer, Moritz Helias |
| 2025 | From Language Models over Tokens to Language Models over Characters. Tim Vieira, Benjamin LeBrun, Mario Giulianelli, Juan Luis Gastaldi, Brian DuSell, John Terilla, Timothy J. O'Donnell, Ryan Cotterell |
| 2025 | From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection. Lincan Cai, Jingxuan Kang, Shuang Li, Wenxuan Ma, Binhui Xie, Zhida Qin, Jian Liang |
| 2025 | From Logits to Hierarchies: Hierarchical Clustering made Simple. Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser, Imant Daunhawer, Julia E. Vogt |
| 2025 | From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications. Ajay Kumar Jaiswal, Yifan Wang, Lu Yin, Shiwei Liu, Runjin Chen, Jiawei Zhao, Ananth Grama, Yuandong Tian, Zhangyang Wang |
| 2025 | From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models. Etowah Adams, Liam Bai, Minji Lee, Yiyang Yu, Mohammed AlQuraishi |
| 2025 | From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information? Zhanke Zhou, Xiao Feng, Zhaocheng Zhu, Jiangchao Yao, Sanmi Koyejo, Bo Han |
| 2025 | From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection. Moritz Vandenhirtz, Julia E. Vogt |
| 2025 | From RAG to Memory: Non-Parametric Continual Learning for Large Language Models. Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, Yu Su |
| 2025 | From Spectrum-free towards Baseline-view-free: Double-track Proximity Driven Multi-view Clustering. Shengju Yu, Zhibin Dong, Siwei Wang, Suyuan Liu, Ke Liang, Xinwang Liu, Yue Liu, Yi Zhang |
| 2025 | From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers. Yoon Hyeok Lee, Jaemin Park, Taejin Paik, Doyun Kim, Bosun Hwang |
| 2025 | From Thousands to Billions: 3D Visual Language Grounding via Render-Supervised Distillation from 2D VLMs. Ang Cao, Sergio Arnaud, Oleksandr Maksymets, Jianing Yang, Ayush Jain, Ada Martin, Vincent-Pierre Berges, Paul McVay, Ruslan Partsey, Aravind Rajeswaran, Franziska Meier, Justin Johnson, Jeong Joon Park, Alexander Sax |
| 2025 | From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining. Fuying Wang, Jiacheng Xu, Lequan Yu |
| 2025 | From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control. Peiyan Hu, Xiaowei Qian, Wenhao Deng, Rui Wang, Haodong Feng, Ruiqi Feng, Tao Zhang, Long Wei, Yue Wang, Zhi-Ming Ma, Tailin Wu |
| 2025 | From Weight-Based to State-Based Fine-Tuning: Further Memory Reduction on LoRA with Parallel Control. Chi Zhang, Lianhai Ren, Jingpu Cheng, Qianxiao Li |
| 2025 | Fully Dynamic Embedding into ℓp Spaces. Kiarash Banihashem, Xiang Chen, MohammadTaghi Hajiaghayi, Sungchul Kim, Kanak Mahadik, Ryan A. Rossi, Tong Yu |
| 2025 | Fully Dynamic Euclidean Bi-Chromatic Matching in Sublinear Update Time. Gramoz Goranci, Peter Kiss, Neel Patel, Martin P. Seybold, Eva Szilagyi, Da Wei Zheng |
| 2025 | Fully Heteroscedastic Count Regression with Deep Double Poisson Networks. Spencer Young, Porter Jenkins, Longchao Da, Jeffrey Dotson, Hua Wei |
| 2025 | FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch. Virginia Aglietti, Ira Ktena, Jessica Schrouff, Eleni Sgouritsa, Francisco J. R. Ruiz, Alan Malek, Alexis Bellot, Silvia Chiappa |
| 2025 | Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces. Tyler Ingebrand, Adam J. Thorpe, Ufuk Topcu |
| 2025 | Function-Space Learning Rates. Edward Milsom, Ben Anson, Laurence Aitchison |
| 2025 | Function-to-Style Guidance of LLMs for Code Translation. Longhui Zhang, Bin Wang, Jiahao Wang, Xiaofeng Zhao, Min Zhang, Hao Yang, Meishan Zhang, Yu Li, Jing Li, Jun Yu, Min Zhang |
| 2025 | Functional Alignment Can Mislead: Examining Model Stitching. Damian Smith, Harvey Mannering, Antonia Marcu |
| 2025 | Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton. Chengmei Niu, Zhenyu Liao, Zenan Ling, Michael W. Mahoney |
| 2025 | Fundamental Limits of Visual Autoregressive Transformers: Universal Approximation Abilities. Yifang Chen, Xiaoyu Li, Yingyu Liang, Zhenmei Shi, Zhao Song |
| 2025 | Fundamental limits of learning in sequence multi-index models and deep attention networks: high-dimensional asymptotics and sharp thresholds. Emanuele Troiani, Hugo Cui, Yatin Dandi, Florent Krzakala, Lenka Zdeborová |
| 2025 | FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks. Quansong He, Xiangde Min, Kaishen Wang, Tao He |
| 2025 | Fusing Reward and Dueling Feedback in Stochastic Bandits. Xuchuang Wang, Qirun Zeng, Jinhang Zuo, Xutong Liu, Mohammad Hajiesmaili, John C. S. Lui, Adam Wierman |
| 2025 | G-Adaptivity: optimised graph-based mesh relocation for finite element methods. James Rowbottom, Georg Maierhofer, Teo Deveney, Eike Hermann Müller, Alberto Paganini, Katharina Schratz, Pietro Lio, Carola-Bibiane Schönlieb, Chris J. Budd |
| 2025 | G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks. Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, Dawei Cheng |
| 2025 | G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration. Samuel Holt, Max Ruiz Luyten, Antonin Berthon, Mihaela van der Schaar |
| 2025 | GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language Models. Pengxiang Zhao, Xiaoming Yuan |
| 2025 | GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model. Zixiang Ai, Zichen Liu, Yuanhang Lei, Zhenyu Cui, Xu Zou, Jiahuan Zhou |
| 2025 | GCAL: Adapting Graph Models to Evolving Domain Shifts. Ziyue Qiao, Qianyi Cai, Hao Dong, Jiawei Gu, Pengyang Wang, Meng Xiao, Xiao Luo, Hui Xiong |
| 2025 | GEFA: A General Feature Attribution Framework Using Proxy Gradient Estimation. Yi Cai, Thibaud Ardoin, Gerhard Wunder |
| 2025 | GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention. Jiaru Qian, Guancheng Wan, Wenke Huang, Guibin Zhang, Yuxin Wu, Bo Du, Mang Ye |
| 2025 | GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation. Jiashu He, Mingyu Derek Ma, Jinxuan Fan, Dan Roth, Wei Wang, Alejandro Ribeiro |
| 2025 | GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras. Ekaterina Filimoshina, Dmitry Shirokov |
| 2025 | GMAIL: Generative Modality Alignment for generated Image Learning. Shentong Mo, Sukmin Yun |
| 2025 | GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning. Nannan Wu, Yuming Huang, Yiming Zhao, Jie Chen, Wenjun Wang |
| 2025 | GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration. Yuhang Li, Ruokai Yin, Donghyun Lee, Shiting Xiao, Priyadarshini Panda |
| 2025 | GRADEO: Towards Human-Like Evaluation for Text-to-Video Generation via Multi-Step Reasoning. Zhun Mou, Bin Xia, Zhengchao Huang, Wenming Yang, Jiaya Jia |
| 2025 | GRAIL: Graph Edit Distance and Node Alignment using LLM-Generated Code. Samidha Verma, Arushi Goyal, Ananya Mathur, Ankit Anand, Sayan Ranu |
| 2025 | GRAM: A Generative Foundation Reward Model for Reward Generalization. Chenglong Wang, Yang Gan, Yifu Huo, Yongyu Mu, Qiaozhi He, Murun Yang, Bei Li, Tong Xiao, Chunliang Zhang, Tongran Liu, Jingbo Zhu |
| 2025 | GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs. Yue Wang, Qizhou Wang, Feng Liu, Wei Huang, Yali Du, Xiaojiang Du, Bo Han |
| 2025 | GS-Bias: Global-Spatial Bias Learner for Single-Image Test-Time Adaptation of Vision-Language Models. Zhaohong Huang, Yuxin Zhang, Jingjing Xie, Fei Chao, Rongrong Ji |
| 2025 | GSM-∞: How Do your LLMs Behave over Infinitely Increasing Reasoning Complexity and Context Length? Yang Zhou, Hongyi Liu, Zhuoming Chen, Yuandong Tian, Beidi Chen |
| 2025 | GTR: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning. Xiangheng Wang, Ziquan Fang, Chenglong Huang, Danlei Hu, Lu Chen, Yunjun Gao |
| 2025 | Galileo: Learning Global & Local Features of Many Remote Sensing Modalities. Gabriel Tseng, Anthony Fuller, Marlena Reil, Henry Herzog, Patrick Beukema, Favyen Bastani, James R. Green, Evan Shelhamer, Hannah Kerner, David Rolnick |
| 2025 | Gamma Distribution PCA-Enhanced Feature Learning for Angle-Robust SAR Target Recognition. Chong Zhang, Peng Zhang, Mengke Li |
| 2025 | Gandalf the Red: Adaptive Security for LLMs. Niklas Pfister, Václav Volhejn, Manuel Knott, Santiago Arias, Julia Bazinska, Mykhailo Bichurin, Alan Y. Commike, Janet Darling, Peter Dienes, Matthew Fiedler, David Haber, Matthias Kraft, Marco Lancini, Max Mathys, Damián Pascual-Ortiz, Jakub Podolak, Adrià Romero-López, Kyriacos Shiarlis, Andreas Signer, Zsolt Terek, Athanasios Theocharis, Daniel Timbrell, Samuel Trautwein, Samuel Watts, Yun-Han Wu, Mateo Rojas-Carulla |
| 2025 | Gap-Dependent Bounds for Federated Q-Learning. Haochen Zhang, Zhong Zheng, Lingzhou Xue |
| 2025 | GaussMark: A Practical Approach for Structural Watermarking of Language Models. Adam Block, Alexander Rakhlin, Ayush Sekhari |
| 2025 | GaussMarker: Robust Dual-Domain Watermark for Diffusion Models. Kecen Li, Zhicong Huang, Xinwen Hou, Cheng Hong |
| 2025 | Gaussian Mixture Flow Matching Models. Hansheng Chen, Kai Zhang, Hao Tan, Zexiang Xu, Fujun Luan, Leonidas J. Guibas, Gordon Wetzstein, Sai Bi |
| 2025 | GenMol: A Drug Discovery Generalist with Discrete Diffusion. Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Yuxing Peng, Saee Gopal Paliwal, Weili Nie, Arash Vahdat |
| 2025 | GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder. Shiming Chen, Dingjie Fu, Salman Khan, Fahad Shahbaz Khan |
| 2025 | General agents need world models. Jonathan Richens, Tom Everitt, David Abel |
| 2025 | General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization. Kwangjun Ahn, Gagik Magakyan, Ashok Cutkosky |
| 2025 | Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks. Angelica Chen, Samuel Don Stanton, Frances Ding, Robert G. Alberstein, Andrew Martin Watkins, Richard Bonneau, Vladimir Gligorijevic, Kyunghyun Cho, Nathan C. Frey |
| 2025 | Generalizable Multi-Camera 3D Object Detection from a Single Source via Fourier Cross-View Learning. Xue Zhao, Qinying Gu, Xinbing Wang, Chenghu Zhou, Nanyang Ye |
| 2025 | Generalization Analysis for Controllable Learning. Yifan Zhang, Xiao Zhang, Min-Ling Zhang |
| 2025 | Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings. Nong Minh Hieu, Antoine Ledent |
| 2025 | Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks. Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours, Alexandre Drouin, Pascal Germain |
| 2025 | Generalization Performance of Ensemble Clustering: From Theory to Algorithm. Xu Zhang, Haoye Qiu, Weixuan Liang, Hui Liu, Junhui Hou, Yuheng Jia |
| 2025 | Generalization Principles for Inference over Text-Attributed Graphs with Large Language Models. Haoyu Peter Wang, Shikun Liu, Rongzhe Wei, Pan Li |
| 2025 | Generalization and Robustness of the Tilted Empirical Risk. Gholamali Aminian, Amir R. Asadi, Tian Li, Ahmad Beirami, Gesine Reinert, Samuel N. Cohen |
| 2025 | Generalization in Federated Learning: A Conditional Mutual Information Framework. Ziqiao Wang, Cheng Long, Yongyi Mao |
| 2025 | Generalization of noisy SGD in unbounded non-convex settings. Leello Tadesse Dadi, Volkan Cevher |
| 2025 | Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization. Duo Liu, Zhiquan Tan, Linglan Zhao, Zhongqiang Zhang, Xiangzhong Fang, Weiran Huang |
| 2025 | Generalized Interpolating Discrete Diffusion. Dimitri von Rütte, Janis Fluri, Yuhui Ding, Antonio Orvieto, Bernhard Schölkopf, Thomas Hofmann |
| 2025 | Generalized Random Forests Using Fixed-Point Trees. David Fleischer, David A. Stephens, Archer Y. Yang |
| 2025 | Generalized Smooth Bilevel Optimization with Nonconvex Lower-Level. Siqi Zhang, Xing Huang, Feihu Huang |
| 2025 | Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction. Lars van der Laan, Ahmed M. Alaa |
| 2025 | Generalized additive models via direct optimization of regularized decision stump forests. Magzhan Gabidolla, Miguel Á. Carreira-Perpiñán |
| 2025 | Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments. Baohong Li, Yingrong Wang, Anpeng Wu, Ming Ma, Ruoxuan Xiong, Kun Kuang |
| 2025 | Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs? Simon Park, Abhishek Panigrahi, Yun Cheng, Dingli Yu, Anirudh Goyal, Sanjeev Arora |
| 2025 | Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series. Zachary C. Brown, David Carlson |
| 2025 | Generation from Noisy Examples. Ananth Raman, Vinod Raman |
| 2025 | Generative Audio Language Modeling with Continuous-valued Tokens and Masked Next-Token Prediction. Shu-Wen Yang, Byeonggeun Kim, Kuan-Po Huang, Qingming Tang, Huy Phan, Bo-Ru Lu, Harshavardhan Sundar, Shalini Ghosh, Hung-yi Lee, Chieh-Chi Kao, Chao Wang |
| 2025 | Generative Data Mining with Longtail-Guided Diffusion. David S. Hayden, Mao Ye, Timur Garipov, Gregory P. Meyer, Carl Vondrick, Zhao Chen, Yuning Chai, Eric M. Wolff, Siddhartha S. Srinivasa |
| 2025 | Generative Human Trajectory Recovery via Embedding-Space Conditional Diffusion. Kaijun Liu, Sijie Ruan, Liang Zhang, Cheng Long, Shuliang Wang, Liang Yu |
| 2025 | Generative Intervention Models for Causal Perturbation Modeling. Nora Schneider, Lars Lorch, Niki Kilbertus, Bernhard Schölkopf, Andreas Krause |
| 2025 | Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning. Yang Li, Jiale Ma, Yebin Yang, Qitian Wu, Hongyuan Zha, Junchi Yan |
| 2025 | Generative Point Cloud Registration. Haobo Jiang, Jin Xie, Jian Yang, Liang Yu, Jianmin Zheng |
| 2025 | Generative Social Choice: The Next Generation. Niclas Boehmer, Sara Fish, Ariel D. Procaccia |
| 2025 | GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing. Akashah Shabbir, Mohammed Zumri, Mohammed Bennamoun, Fahad Shahbaz Khan, Salman Khan |
| 2025 | Geometric Algebra Planes: Convex Implicit Neural Volumes. Irmak Sivgin, Sara Fridovich-Keil, Gordon Wetzstein, Mert Pilanci |
| 2025 | Geometric Contact Flows: Contactomorphisms for Dynamics and Control. Andrea Testa, Søren Hauberg, Tamim Asfour, Leonel Rozo |
| 2025 | Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning. Xiangqi Li, Libo Huang, Zhulin An, Weilun Feng, Chuanguang Yang, Boyu Diao, Fei Wang, Yongjun Xu |
| 2025 | Geometric Generative Modeling with Noise-Conditioned Graph Networks. Peter Pao-Huang, Mitchell Black, Xiaojie Qiu |
| 2025 | Geometric Hyena Networks for Large-scale Equivariant Learning. Artem Moskalev, Mangal Prakash, Junjie Xu, Tianyu Cui, Rui Liao, Tommaso Mansi |
| 2025 | Geometric Median (GM) Matching for Robust k-Subset Selection from Noisy Data. Anish Acharya, Sujay Sanghavi, Alex Dimakis, Inderjit S. Dhillon |
| 2025 | Geometric Representation Condition Improves Equivariant Molecule Generation. Zian Li, Cai Zhou, Xiyuan Wang, Xingang Peng, Muhan Zhang |
| 2025 | Geometric Resampling in Nearly Linear Time for Follow-the-Perturbed-Leader with Best-of-Both-Worlds Guarantee in Bandit Problems. Botao Chen, Jongyeong Lee, Junya Honda |
| 2025 | Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates. Yunfei Huang, David S. Greenberg |
| 2025 | Geometry Informed Tokenization of Molecules for Language Model Generation. Xiner Li, Limei Wang, Youzhi Luo, Carl Edwards, Shurui Gui, Yuchao Lin, Heng Ji, Shuiwang Ji |
| 2025 | Geometry-Informed Neural Networks. Arturs Berzins, Andreas Radler, Eric Volkmann, Sebastian Sanokowski, Sepp Hochreiter, Johannes Brandstetter |
| 2025 | Global Context-aware Representation Learning for Spatially Resolved Transcriptomics. Yunhak Oh, Junseok Lee, Yeongmin Kim, Sangwoo Seo, Namkyeong Lee, Chanyoung Park |
| 2025 | Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μ Parametrization. Zixiang Chen, Greg Yang, Qingyue Zhao, Quanquan Gu |
| 2025 | Global Optimization with a Power-Transformed Objective and Gaussian Smoothing. Chen Xu |
| 2025 | Global curvature for second-order optimization of neural networks. Alberto Bernacchia |
| 2025 | Global-Local Dirichlet Processes for Clustering Grouped Data in the Presence of Group-Specific Idiosyncratic Variables. Arhit Chakrabarti, Yang Ni, Debdeep Pati, Bani K. Mallick |
| 2025 | GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction. Muleilan Pei, Shaoshuai Shi, Lu Zhang, Peiliang Li, Shaojie Shen |
| 2025 | Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning. Jinmin He, Kai Li, Yifan Zang, Haobo Fu, Qiang Fu, Junliang Xing, Jian Cheng |
| 2025 | Going Deeper into Locally Differentially Private Graph Neural Networks. Longzhu He, Chaozhuo Li, Peng Tang, Sen Su |
| 2025 | GradPS: Resolving Futile Neurons in Parameter Sharing Network for Multi-Agent Reinforcement Learning. Haoyuan Qin, Zhengzhu Liu, Chenxing Lin, Chennan Ma, Songzhu Mei, Siqi Shen, Cheng Wang |
| 2025 | Gradient Aligned Regression via Pairwise Losses. Dixian Zhu, Tianbao Yang, Livnat Jerby |
| 2025 | Gradient Boosting Reinforcement Learning. Benjamin Fuhrer, Chen Tessler, Gal Dalal |
| 2025 | Gradient Descent Converges Arbitrarily Fast for Logistic Regression via Large and Adaptive Stepsizes. Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett |
| 2025 | Gradient Flow Provably Learns Robust Classifiers for Orthonormal GMMs. Hancheng Min, René Vidal |
| 2025 | Gradient Inversion of Multimodal Models. Omri Ben Hemo, Alon Zolfi, Oryan Yehezkel, Omer Hofman, Roman Vainshtein, Hisashi Kojima, Yuval Elovici, Asaf Shabtai |
| 2025 | Gradient-based Explanations for Deep Learning Survival Models. Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright |
| 2025 | Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning. Motoki Omura, Kazuki Ota, Takayuki Osa, Yusuke Mukuta, Tatsuya Harada |
| 2025 | Grammar-Forced Translation of Natural Language to Temporal Logic using LLMs. William H. English, Dominic Simon, Sumit Kumar Jha, Rickard Ewetz |
| 2025 | Graph Adaptive Autoregressive Moving Average Models. Moshe Eliasof, Alessio Gravina, Andrea Ceni, Claudio Gallicchio, Davide Bacciu, Carola-Bibiane Schönlieb |
| 2025 | Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models. Zhongtian Ma, Qiaosheng Zhang, Bocheng Zhou, Yexin Zhang, Shuyue Hu, Zhen Wang |
| 2025 | Graph Diffusion for Robust Multi-Agent Coordination. Xianghua Zeng, Hang Su, Zhengyi Wang, Zhiyuan Lin |
| 2025 | Graph Generative Pre-trained Transformer. Xiaohui Chen, Yinkai Wang, Jiaxing He, Yuanqi Du, Soha Hassoun, Xiaolin Xu, Liping Liu |
| 2025 | Graph Inverse Style Transfer for Counterfactual Explainability. Bardh Prenkaj, Efstratios Zaradoukas, Gjergji Kasneci |
| 2025 | Graph Minimum Factorization Distance and Its Application to Large-Scale Graph Data Clustering. Jicong Fan |
| 2025 | Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation. Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, Amine Mohamed Aboussalah, Michalis Vazirgiannis |
| 2025 | Graph World Model. Tao Feng, Yexin Wu, Guanyu Lin, Jiaxuan You |
| 2025 | Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning. Seungho Baek, Tae-Geon Park, Jongchan Park, Seungjun Oh, Yusung Kim |
| 2025 | Graph-Based Algorithms for Diverse Similarity Search. Piyush Anand, Piotr Indyk, Ravishankar Krishnaswamy, Sepideh Mahabadi, Vikas C. Raykar, Kirankumar Shiragur, Haike Xu |
| 2025 | Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization. Robbert Reijnen, Yaoxin Wu, Zaharah Bukhsh, Yingqian Zhang |
| 2025 | Graph-constrained Reasoning: Faithful Reasoning on Knowledge Graphs with Large Language Models. Linhao Luo, Zicheng Zhao, Gholamreza Haffari, Yuan-Fang Li, Chen Gong, Shirui Pan |
| 2025 | Graph4MM: Weaving Multimodal Learning with Structural Information. Xuying Ning, Dongqi Fu, Tianxin Wei, Wujiang Xu, Jingrui He |
| 2025 | GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation. Mengzhu Wang, Houcheng Su, Jiao Li, Chuan Li, Nan Yin, Li Shen, Jingcai Guo |
| 2025 | GraphGPT: Generative Pre-trained Graph Eulerian Transformer. Qifang Zhao, Weidong Ren, Tianyu Li, Hong Liu, Xingsheng He, Xiaoxiao Xu |
| 2025 | Gravity-Bench-v1: A Benchmark on Gravitational Physics Discovery for Agents. Nolan Koblischke, Hyunseok Jang, Kristen Menou, Mohamad Ali-Dib |
| 2025 | Great Models Think Alike and this Undermines AI Oversight. Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K. Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, Jonas Geiping |
| 2025 | Gridded Transformer Neural Processes for Spatio-Temporal Data. Matthew Ashman, Cristiana Diaconu, Eric Langezaal, Adrian Weller, Richard E. Turner |
| 2025 | Griffin: Towards a Graph-Centric Relational Database Foundation Model. Yanbo Wang, Xiyuan Wang, Quan Gan, Minjie Wang, Qibin Yang, David Wipf, Muhan Zhang |
| 2025 | GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers. GuoguoAi, Guansong Pang, Hezhe Qiao, Yuan Gao, Hui Yan |
| 2025 | Grokking Beyond the Euclidean Norm of Model Parameters. Pascal Tikeng Notsawo Jr., Guillaume Dumas, Guillaume Rabusseau |
| 2025 | Grokking at the Edge of Linear Separability. Alon Beck, Noam Itzhak Levi, Yohai Bar-Sinai |
| 2025 | Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers. Roman Abramov, Felix Steinbauer, Gjergji Kasneci |
| 2025 | Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery. Paris Giampouras, HanQin Cai, René Vidal |
| 2025 | GuardAgent: Safeguard LLM Agents via Knowledge-Enabled Reasoning. Zhen Xiang, Linzhi Zheng, YanJie Li, Junyuan Hong, Qinbin Li, Han Xie, Jiawei Zhang, Zidi Xiong, Chulin Xie, Carl Yang, Dawn Song, Bo Li |
| 2025 | Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics. Aleksandr Gushchin, Khaled Abud, Georgii Bychkov, Ekaterina Shumitskaya, Anna Chistyakova, Sergey Lavrushkin, Bader Rasheed, Kirill Malyshev, Dmitriy S. Vatolin, Anastasia Antsiferova |
| 2025 | Guided Search Strategies in Non-Serializable Environments with Applications to Software Engineering Agents. Karina Zainullina, Alexander Golubev, Maria Trofimova, Sergei Polezhaev, Ibragim Badertdinov, Daria Litvintseva, Simon Karasik, Filipp Fisin, Sergei Skvortsov, Maksim Nekrashevich, Anton Shevtsov, Boris Yangel |
| 2025 | Guided Structural Inference: Leveraging Priors with Soft Gating Mechanisms. Aoran Wang, Xinnan Dai, Jun Pang |
| 2025 | Guided Zeroth-Order Methods for Stochastic Non-convex Problems with Decision-Dependent Distributions. Yuya Hikima, Hiroshi Sawada, Akinori Fujino |
| 2025 | GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance. Jinuk Kim, Marwa El Halabi, Wonpyo Park, Clemens J. S. Schaefer, Deokjae Lee, Yeonhong Park, Jae W. Lee, Hyun Oh Song |
| 2025 | Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding. Jinze Li, Yixing Xu, Haiduo Huang, Xuanwu Yin, Dong Li, Edith C. H. Ngai, Emad Barsoum |
| 2025 | H-Tuning: Toward Low-Cost and Efficient ECG-based Cardiovascular Disease Detection with Pre-Trained Models. Rushuang Zhou, Yuanting Zhang, Yining Dong |
| 2025 | HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training. Geon-Woo Kim, Junbo Li, Shashidhar Gandham, Omar Baldonado, Adithya Gangidi, Pavan Balaji, Zhangyang Wang, Aditya Akella |
| 2025 | HEAP: Hyper Extended A-PDHG Operator for Constrained High-dim PDEs. Mingquan Feng, Weixin Liao, Yixin Huang, Yifan Fu, Qifu Zheng, Junchi Yan |
| 2025 | HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport. Yanbei Liu, Chongxu Wang, Zhitao Xiao, Lei Geng, Yanwei Pang, Xiao Wang |
| 2025 | HPS: Hard Preference Sampling for Human Preference Alignment. Xiandong Zou, Wanyu Lin, Yuchen Li, Pan Zhou |
| 2025 | HYGMA: Hypergraph Coordination Networks with Dynamic Grouping for Multi-Agent Reinforcement Learning. Chiqiang Liu, Dazi Li |
| 2025 | Habitizing Diffusion Planning for Efficient and Effective Decision Making. Haofei Lu, Yifei Shen, Dongsheng Li, Junliang Xing, Dongqi Han |
| 2025 | Handling Imbalanced Pseudolabels for Vision-Language Models with Concept Alignment and Confusion-Aware Calibrated Margin. Yuchen Wang, Xuefeng Bai, Xiucheng Li, Weili Guan, Liqiang Nie, Xinyang Chen |
| 2025 | HaploVL: A Single-Transformer Baseline for Multi-Modal Understanding. Rui Yang, Lin Song, Yicheng Xiao, Runhui Huang, Yixiao Ge, Ying Shan, Hengshuang Zhao |
| 2025 | Hardware and Software Platform Inference. Cheng Zhang, Hanna Foerster, Robert D. Mullins, Yiren Zhao, Ilia Shumailov |
| 2025 | HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer Acceleration. Yushi Huang, Zining Wang, Ruihao Gong, Jing Liu, Xinjie Zhang, Jinyang Guo, Xianglong Liu, Jun Zhang |
| 2025 | Harmonizing Geometry and Uncertainty: Diffusion with Hyperspheres. Muskan Dosi, Chiranjeev Chiranjeev, Kartik Thakral, Mayank Vatsa, Richa Singh |
| 2025 | Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference. Mahendra Singh Thapa, Rui Li |
| 2025 | HashAttention: Semantic Sparsity for Faster Inference. Aditya Desai, Shuo Yang, Alejandro Cuadron, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica |
| 2025 | Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks. Rui Xue, Tong Zhao, Neil Shah, Xiaorui Liu |
| 2025 | Heads up! Large Language Models Can Perform Tasks Without Your Instruction via Selective Attention Head Masking. Senyu Han, Hongchuan Zeng, Kai Yu, Lu Chen |
| 2025 | HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation. Tianwei Lin, Wenqiao Zhang, Sijing Li, Yuqian Yuan, Binhe Yu, Haoyuan Li, Wanggui He, Hao Jiang, Mengze Li, Xiaohui Song, Siliang Tang, Jun Xiao, Hui Lin, Yueting Zhuang, Beng Chin Ooi |
| 2025 | Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update. Jing Wang, Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou |
| 2025 | Hessian Geometry of Latent Space in Generative Models. Alexander Lobashev, Dmitry Guskov, Maria A. Larchenko, Mikhail V. Tamm |
| 2025 | HetSSNet: Spatial-Spectral Heterogeneous Graph Learning Network for Panchromatic and Multispectral Images Fusion. Mengting Ma, Yizhen Jiang, Mengjiao Zhao, Jiaxin Li, Wei Zhang |
| 2025 | Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources. Renzhe Xu, Kang Wang, Bo Li |
| 2025 | Heterogeneous Label Shift: Theory and Algorithm. Chao Xu, Xijia Tang, Chenping Hou |
| 2025 | Heterogeneous Sufficient Dimension Reduction and Subspace Clustering. Lei Yan, Xin Zhang, Qing Mai |
| 2025 | Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees. Tomer Meir, Uri Shalit, Malka Gorfine |
| 2025 | Hgformer: Hyperbolic Graph Transformer for Collaborative Filtering. Xin Yang, Xingrun Li, Heng Chang, Jinze Yang, Xihong Yang, Shengyu Tao, Maiko Shigeno, Ningkang Chang, Junfeng Wang, Dawei Yin, Erxue Min |
| 2025 | Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models. Lucy Xiaoyang Shi, Brian Ichter, Michael Robert Equi, Liyiming Ke, Karl Pertsch, Quan Vuong, James Tanner, Anna Walling, Haohuan Wang, Niccolo Fusai, Adrian Li-Bell, Danny Driess, Lachy Groom, Sergey Levine, Chelsea Finn |
| 2025 | Hi-Patch: Hierarchical Patch GNN for Irregular Multivariate Time Series. Yicheng Luo, Bowen Zhang, Zhen Liu, Qianli Ma |
| 2025 | HiRemate: Hierarchical Approach for Efficient Re-materialization of Neural Networks. Julia Gusak, Xunyi Zhao, Théotime Le Hellard, Zhe Li, Lionel Eyraud-Dubois, Olivier Beaumont |
| 2025 | Hidden No More: Attacking and Defending Private Third-Party LLM Inference. Rahul Krishna Thomas, Louai Zahran, Erica Choi, Akilesh Potti, Micah Goldblum, Arka Pal |
| 2025 | Hide & Seek: Transformer Symmetries Obscure Sharpness & Riemannian Geometry Finds It. Marvin F. da Silva, Felix Dangel, Sageev Oore |
| 2025 | Hierarchical Equivariant Policy via Frame Transfer. Haibo Zhao, Dian Wang, Yizhe Zhu, Xupeng Zhu, Owen Lewis Howell, Linfeng Zhao, Yaoyao Qian, Robin Walters, Robert Platt |
| 2025 | Hierarchical Graph Tokenization for Molecule-Language Alignment. Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian |
| 2025 | Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots. Guangting Zheng, Yehao Li, Yingwei Pan, Jiajun Deng, Ting Yao, Yanyong Zhang, Tao Mei |
| 2025 | Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability. Yicheng Pan, Renjie Chen, Pengyu Long, Bingchen Fan |
| 2025 | Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verification. Flavio Petruzzellis, Cristina Cornelio, Pietro Lio |
| 2025 | Hierarchical Refinement: Optimal Transport to Infinity and Beyond. Peter Halmos, Julian Gold, Xinhao Liu, Benjamin J. Raphael |
| 2025 | Hierarchical Reinforcement Learning with Targeted Causal Interventions. Mohammadsadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Matthias Grossglauser |
| 2025 | Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals. Vivienne Huiling Wang, Tinghuai Wang, Joni Pajarinen |
| 2025 | High Dynamic Range Novel View Synthesis with Single Exposure. Kaixuan Zhang, Hu Wang, Minxian Li, Mingwu Ren, Mao Ye, Xiatian Zhu |
| 2025 | High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions. Ruiyuan Huang, Zengfeng Huang |
| 2025 | High-Dimensional Prediction for Sequential Decision Making. Georgy Noarov, Ramya Ramalingam, Aaron Roth, Stephan Xie |
| 2025 | High-Dimensional Tensor Regression With Oracle Properties. Wenbin Wang, Yu Shi, Ziping Zhao |
| 2025 | High-Fidelity Simultaneous Speech-To-Speech Translation. Tom Labiausse, Laurent Mazaré, Edouard Grave, Alexandre Défossez, Neil Zeghidour |
| 2025 | Highly Compressed Tokenizer Can Generate Without Training. Lukas Lao Beyer, Tianhong Li, Xinlei Chen, Sertac Karaman, Kaiming He |
| 2025 | History-Guided Video Diffusion. Kiwhan Song, Boyuan Chen, Max Simchowitz, Yilun Du, Russ Tedrake, Vincent Sitzmann |
| 2025 | Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space. Xihang Yue, Yi Yang, Linchao Zhu |
| 2025 | Homophily Enhanced Graph Domain Adaptation. Ruiyi Fang, Bingheng Li, Jingyu Zhao, Ruizhi Pu, Qiuhao Zeng, Gezheng Xu, Charles Ling, Boyu Wang |
| 2025 | How Compositional Generalization and Creativity Improve as Diffusion Models are Trained. Alessandro Favero, Antonio Sclocchi, Francesco Cagnetta, Pascal Frossard, Matthieu Wyart |
| 2025 | How Contaminated Is Your Benchmark? Measuring Dataset Leakage in Large Language Models with Kernel Divergence. Hyeong Kyu Choi, Maxim Khanov, Hongxin Wei, Yixuan Li |
| 2025 | How Distributed Collaboration Influences the Diffusion Model Training? A Theoretical Perspective. Jing Qiao, Yu Liu, Yuan Yuan, Xiao Zhang, Zhipeng Cai, Dongxiao Yu |
| 2025 | How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation. Yining Pan, Qiongjie Cui, XuLei Yang, Na Zhao |
| 2025 | How Do Large Language Monkeys Get Their Power (Laws)? Rylan Schaeffer, Joshua Kazdan, John Hughes, Jordan Juravsky, Sara Price, Aengus Lynch, Erik Jones, Robert Kirk, Azalia Mirhoseini, Sanmi Koyejo |
| 2025 | How Do Transformers Learn Variable Binding in Symbolic Programs? Yiwei Wu, Atticus Geiger, Raphaël Millière |
| 2025 | How Effective Can Dropout Be in Multiple Instance Learning ? Wenhui Zhu, Peijie Qiu, Xiwen Chen, Zhangsihao Yang, Aristeidis Sotiras, Abolfazl Razi, Yalin Wang |
| 2025 | How Expressive are Knowledge Graph Foundation Models? Xingyue Huang, Pablo Barceló, Michael M. Bronstein, Ismail Ilkan Ceylan, Mikhail Galkin, Juan L. Reutter, Miguel A. Romero Orth |
| 2025 | How Far Is Video Generation from World Model: A Physical Law Perspective. Bingyi Kang, Yang Yue, Rui Lu, Zhijie Lin, Yang Zhao, Kaixin Wang, Gao Huang, Jiashi Feng |
| 2025 | How Much Can Transfer? BRIDGE: Bounded Multi-Domain Graph Foundation Model with Generalization Guarantees. Haonan Yuan, Qingyun Sun, Junhua Shi, Xingcheng Fu, Bryan Hooi, Jianxin Li, Philip S. Yu |
| 2025 | How Much Can We Forget about Data Contamination? Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko, Ulrike von Luxburg |
| 2025 | How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias. Ruiquan Huang, Yingbin Liang, Jing Yang |
| 2025 | How Transformers Learn Structured Data: Insights From Hierarchical Filtering. Jerome Garnier-Brun, Marc Mézard, Emanuele Moscato, Luca Saglietti |
| 2025 | How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction. Jun Chen, Hong Chen, Yonghua Yu, Yiming Ying |
| 2025 | How to Evaluate and Mitigate IP Infringement in Visual Generative AI? Zhenting Wang, Chen Chen, Vikash Sehwag, Minzhou Pan, Lingjuan Lyu |
| 2025 | How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects. Wonkwang Lee, Jongwon Jeong, Taehong Moon, Hyeon-Jong Kim, Jaehyeon Kim, Gunhee Kim, Byeong-Uk Lee |
| 2025 | How to Synthesize Text Data without Model Collapse? Xuekai Zhu, Daixuan Cheng, Hengli Li, Kaiyan Zhang, Ermo Hua, Xingtai Lv, Ning Ding, Zhouhan Lin, Zilong Zheng, Bowen Zhou |
| 2025 | How to Train Your Multi-Exit Model? Analyzing the Impact of Training Strategies. Piotr Kubaty, Bartosz Wójcik, Bartlomiej Krzepkowski, Monika Michaluk, Tomasz Trzcinski, Jary Pomponi, Kamil Adamczewski |
| 2025 | How to set AdamW's weight decay as you scale model and dataset size. Xi Wang, Laurence Aitchison |
| 2025 | Human Body Restoration with One-Step Diffusion Model and A New Benchmark. Jue Gong, Jingkai Wang, Zheng Chen, Xin Liu, Hong Gu, Yulun Zhang, Xiaokang Yang |
| 2025 | Human Cognition-Inspired Hierarchical Fuzzy Learning Machine. Junbiao Cui, Qin Yue, Jianqing Liang, Jiye Liang |
| 2025 | Human-Aligned Image Models Improve Visual Decoding from the Brain. Nona Rajabi, Antônio H. Ribeiro, Miguel Vasco, Farzaneh Taleb, Mårten Björkman, Danica Kragic |
| 2025 | Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning. Hongyao Chen, Tianyang Xu, Xiaojun Wu, Josef Kittler |
| 2025 | Hybrid Quantum-Classical Multi-Agent Pathfinding. Thore Gerlach, Loong Kuan Lee, Frédéric Barbaresco, Nico Piatkowski |
| 2025 | Hybrid Spiking Vision Transformer for Object Detection with Event Cameras. Qi Xu, Jie Deng, Jiangrong Shen, Biwu Chen, Huajin Tang, Gang Pan |
| 2025 | HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder. Qi Yang, Le Yang, Geert Van der Auwera, Zhu Li |
| 2025 | Hyper-Transforming Latent Diffusion Models. Ignacio Peis, Batuhan Koyuncu, Isabel Valera, Jes Frellsen |
| 2025 | Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning. Yiran Wang, Chenshu Liu, Yunfan Li, Sanae Amani, Bolei Zhou, Lin Yang |
| 2025 | HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting. Boyuan Li, Yicheng Luo, Zhen Liu, Junhao Zheng, Jianming Lv, Qianli Ma |
| 2025 | HyperIV: Real-time Implied Volatility Smoothing. Yongxin Yang, Wenqi Chen, Chao Shu, Timothy M. Hospedales |
| 2025 | HyperNear: Unnoticeable Node Injection Attacks on Hypergraph Neural Networks. Tingyi Cai, Yunliang Jiang, Ming Li, Lu Bai, Changqin Huang, Yi Wang |
| 2025 | HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking. Runquan Gui, Zhihai Wang, Jie Wang, Chi Ma, Huiling Zhen, Mingxuan Yuan, Jianye Hao, Defu Lian, Enhong Chen, Feng Wu |
| 2025 | Hyperband-based Bayesian Optimization for Black-box Prompt Selection. Lennart Schneider, Martin Wistuba, Aaron Klein, Jacek Golebiowski, Giovanni Zappella, Felice Antonio Merra |
| 2025 | Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations. Juwei Yue, Haikuo Li, Jiawei Sheng, Xiaodong Li, Taoyu Su, Tingwen Liu, Li Guo |
| 2025 | Hyperspherical Normalization for Scalable Deep Reinforcement Learning. Hojoon Lee, Youngdo Lee, Takuma Seno, Donghu Kim, Peter Stone, Jaegul Choo |
| 2025 | Hypo3D: Exploring Hypothetical Reasoning in 3D. Ye Mao, Weixun Luo, Junpeng Jing, Anlan Qiu, Krystian Mikolajczyk |
| 2025 | Hypothesis Testing for Generalized Thurstone Models. Anuran Makur, Japneet Singh |
| 2025 | I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models. Zhenxing Mi, Kuan-Chieh Wang, Guocheng Qian, Hanrong Ye, Runtao Liu, Sergey Tulyakov, Kfir Aberman, Dan Xu |
| 2025 | I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts. Jiayi Xin, Sukwon Yun, Jie Peng, Inyoung Choi, Jenna L. Ballard, Tianlong Chen, Qi Long |
| 2025 | IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck. Tian Bian, Yifan Niu, Chaohao Yuan, Chengzhi Piao, Bingzhe Wu, Long-Kai Huang, Yu Rong, Tingyang Xu, Hong Cheng, Jia Li |
| 2025 | ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks. Zhiyao Ren, Siyuan Liang, Aishan Liu, Dacheng Tao |
| 2025 | IL-SOAR : Imitation Learning with Soft Optimistic Actor cRitic. Stefano Viel, Luca Viano, Volkan Cevher |
| 2025 | IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling. Kuan-Po Huang, Shu-Wen Yang, Huy Phan, Bo-Ru Lu, Byeonggeun Kim, Sashank Macha, Qingming Tang, Shalini Ghosh, Hung-yi Lee, Chieh-Chi Kao, Chao Wang |
| 2025 | IMTS is Worth Time × Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction. Zhangyi Hu, Jiemin Wu, Hua Xu, Mingqian Liao, Ninghui Feng, Bo Gao, Songning Lai, Yutao Yue |
| 2025 | INRFlow: Flow Matching for INRs in Ambient Space. Yuyang Wang, Anurag Ranjan, Joshua M. Susskind, Miguel Ángel Bautista |
| 2025 | IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models. Hanting Wang, Tao Jin, Wang Lin, Shulei Wang, Hai Huang, Shengpeng Ji, Zhou Zhao |
| 2025 | IT3: Idempotent Test-Time Training. Nikita Durasov, Assaf Shocher, Doruk Öner, Gal Chechik, Alexei A. Efros, Pascal Fua |
| 2025 | ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks. Saurabh Jha, Rohan R. Arora, Yuji Watanabe, Takumi Yanagawa, Yinfang Chen, Jackson Clark, Bhavya, Mudit Verma, Harshit Kumar, Hirokuni Kitahara, Noah Zheutlin, Saki Takano, Divya Pathak, Felix George, Xinbo Wu, Bekir O. Turkkan, Gerard Vanloo, Michael Nidd, Ting Dai, Oishik Chatterjee, Pranjal Gupta, Suranjana Samanta, Pooja Aggarwal, Rong Lee, Jae-wook Ahn, Debanjana Kar, Amit M. Paradkar, Yu Deng, Pratibha Moogi, Prateeti Mohapatra, Naoki Abe, Chandrasekhar Narayanaswami, Tianyin Xu, Lav R. Varshney, Ruchi Mahindru, Anca Sailer, Laura Shwartz, Daby Sow, Nicholas C. Fuller, Ruchir Puri |
| 2025 | ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset. Yilin Wang, Peixuan Lei, Jie Song, Yuzhe Hao, Tao Chen, Yuxuan Zhang, Lei Jia, Yuanxiang Li, Zhongyu Wei |
| 2025 | Identifiable Object Representations under Spatial Ambiguities. Avinash Kori, Francesca Toni, Ben Glocker |
| 2025 | Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations. Zhengming Chen, Yewei Xia, Feng Xie, Jie Qiao, Zhifeng Hao, Ruichu Cai, Kun Zhang |
| 2025 | Identifying Causal Direction via Variational Bayesian Compression. Quang-Duy Tran, Bao Duong, Phuoc Nguyen, Thin Nguyen |
| 2025 | Identifying Metric Structures of Deep Latent Variable Models. Stas Syrota, Yevgen Zainchkovskyy, Johnny Xi, Benjamin Bloem-Reddy, Søren Hauberg |
| 2025 | Identifying Neural Dynamics Using Interventional State Space Models. Amin Nejatbakhsh, Yixin Wang |
| 2025 | Identifying and Understanding Cross-Class Features in Adversarial Training. Zeming Wei, Steven Y. Guo, Yisen Wang |
| 2025 | Identifying biological perturbation targets through causal differential networks. Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi S. Jaakkola |
| 2025 | Idiosyncrasies in Large Language Models. Mingjie Sun, Yida Yin, Zhiqiu Xu, J. Zico Kolter, Zhuang Liu |
| 2025 | Imagine While Reasoning in Space: Multimodal Visualization-of-Thought. Chengzu Li, Wenshan Wu, Huanyu Zhang, Yan Xia, Shaoguang Mao, Li Dong, Ivan Vulic, Furu Wei |
| 2025 | Imitation Learning from a Single Temporally Misaligned Video. William Huey, Huaxiaoyue Wang, Anne Wu, Yoav Artzi, Sanjiban Choudhury |
| 2025 | Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks. Yuhang Cai, Kangjie Zhou, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett |
| 2025 | Implicit Language Models are RNNs: Balancing Parallelization and Expressivity. Mark Schöne, Babak Rahmani, Heiner Kremer, Fabian Falck, Hitesh Ballani, Jannes Gladrow |
| 2025 | Implicit Regularization for Tubal Tensor Factorizations via Gradient Descent. Santhosh Karnik, Anna Veselovska, Mark A. Iwen, Felix Krahmer |
| 2025 | Implicit Riemannian Optimism with Applications to Min-Max Problems. Christophe Roux, David Martínez-Rubio, Sebastian Pokutta |
| 2025 | Implicit Subgraph Neural Network. Yongjian Zhong, Liao Zhu, Hieu Vu, Bijaya Adhikari |
| 2025 | Implicit degree bias in the link prediction task. Rachith Aiyappa, Xin Wang, Munjung Kim, Ozgur Can Seckin, Yong-Yeol Ahn, Sadamori Kojaku |
| 2025 | Importance Corrected Neural JKO Sampling. Johannes Hertrich, Robert Gruhlke |
| 2025 | Importance Sampling for Nonlinear Models. Prakash Palanivelu Rajmohan, Fred Roosta |
| 2025 | Impossible Videos. Zechen Bai, Hai Ci, Mike Zheng Shou |
| 2025 | Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation. Shyam Nuggehalli, Jifan Zhang, Lalit K. Jain, Robert D. Nowak |
| 2025 | Improved Approximations for Hard Graph Problems using Predictions. Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, Hao Wu |
| 2025 | Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions. Supratim Shit, Gurmehak Kaur Chadha, Surendra Kumar, Bapi Chatterjee |
| 2025 | Improved Discretization Complexity Analysis of Consistency Models: Variance Exploding Forward Process and Decay Discretization Scheme. Ruofeng Yang, Bo Jiang, Cheng Chen, Shuai Li |
| 2025 | Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms. Detian Zhang, Chengqiang Zhang, Yanghui Rao, Li Qing, Chunjiang Zhu |
| 2025 | Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization. Zijian Liu, Zhengyuan Zhou |
| 2025 | Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves. Amer Krivosija, Alexander Munteanu, André Nusser, Chris Schwiegelshohn |
| 2025 | Improved Lower Bounds for First-order Stochastic Non-convex Optimization under Markov Sampling. Zhenyu Sun, Ermin Wei |
| 2025 | Improved Off-policy Reinforcement Learning in Biological Sequence Design. Hyeonah Kim, Minsu Kim, Taeyoung Yun, Sanghyeok Choi, Emmanuel Bengio, Alex Hernández-García, Jinkyoo Park |
| 2025 | Improved Online Confidence Bounds for Multinomial Logistic Bandits. Joongkyu Lee, Min-hwan Oh |
| 2025 | Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance. Shogo Iwazaki, Shion Takeno |
| 2025 | Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization. Guy Kornowski, Daogao Liu, Kunal Talwar |
| 2025 | Improved Theoretically-Grounded Evolutionary Algorithms for Subset Selection with a Linear Cost Constraint. Dan-Xuan Liu, Chao Qian |
| 2025 | Improved and Oracle-Efficient Online ℓ1-Multicalibration. Rohan Ghuge, Vidya Muthukumar, Sahil Singla |
| 2025 | Improving Compositional Generation with Diffusion Models Using Lift Scores. Chenning Yu, Sicun Gao |
| 2025 | Improving Consistency Models with Generator-Augmented Flows. Thibaut Issenhuth, Sangchul Lee, Ludovic Dos Santos, Jean-Yves Franceschi, Chansoo Kim, Alain Rakotomamonjy |
| 2025 | Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers. Filip Szatkowski, Yaoyue Zheng, Fei Yang, Tomasz Trzcinski, Bartlomiej Twardowski, Joost van de Weijer |
| 2025 | Improving Diversity in Language Models: When Temperature Fails, Change the Loss. Alexandre Verine, Florian Le Bronnec, Kunhao Zheng, Alexandre Allauzen, Yann Chevaleyre, Benjamin Négrevergne |
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| 2025 | Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging. Junkang Liu, Yuanyuan Liu, Fanhua Shang, Hongying Liu, Jin Liu, Wei Feng |
| 2025 | Improving Generalization with Flat Hilbert Bayesian Inference. Tuan Truong, Quyen Tran, Ngoc-Quan Pham, Nhat Ho, Dinh Phung, Trung Le |
| 2025 | Improving LLM Safety Alignment with Dual-Objective Optimization. Xuandong Zhao, Will Cai, Tianneng Shi, David Huang, Licong Lin, Song Mei, Dawn Song |
| 2025 | Improving LLM Video Understanding with 16 Frames Per Second. Yixuan Li, Changli Tang, Jimin Zhuang, Yudong Yang, Guangzhi Sun, Wei Li, Zejun Ma, Chao Zhang |
| 2025 | Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens. Ting-Ji Huang, Jia-Qi Yang, Chunxu Shen, Kai-Qi Liu, De-Chuan Zhan, Han-Jia Ye |
| 2025 | Improving Memory Efficiency for Training KANs via Meta Learning. Zhangchi Zhao, Jun Shu, Deyu Meng, Zongben Xu |
| 2025 | Improving Model Alignment Through Collective Intelligence of Open-Source Models. Junlin Wang, Roy Xie, Shang Zhu, Jue Wang, Ben Athiwaratkun, Bhuwan Dhingra, Shuaiwen Leon Song, Ce Zhang, James Zou |
| 2025 | Improving Multi-Class Calibration through Normalization-Aware Isotonic Techniques. Alon Arad, Saharon Rosset |
| 2025 | Improving Multimodal Learning Balance and Sufficiency through Data Remixing. Xiaoyu Ma, Hao Chen, Yongjian Deng |
| 2025 | Improving Out-of-Distribution Detection via Dynamic Covariance Calibration. Kaiyu Guo, Zijian Wang, Tan Pan, Brian C. Lovell, Mahsa Baktashmotlagh |
| 2025 | Improving Out-of-Distribution Detection with Markov Logic Networks. Konstantin Kirchheim, Frank Ortmeier |
| 2025 | Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces. Anjiang Wei, Allen Nie, Thiago S. F. X. Teixeira, Rohan Yadav, Wonchan Lee, Ke Wang, Alex Aiken |
| 2025 | Improving Rationality in the Reasoning Process of Language Models through Self-playing Game. Pinzheng Wang, Juntao Li, Zecheng Tang, Haijia Gui, Min Zhang |
| 2025 | Improving Reward Model Generalization from Adversarial Process Enhanced Preferences. Zhilong Zhang, Tian Xu, Xinghao Du, Xingchen Cao, Yihao Sun, Yang Yu |
| 2025 | Improving Soft Unification with Knowledge Graph Embedding Methods. Xuanming Cui, Chionh Wei Peng, Adriel Kuek, Ser-Nam Lim |
| 2025 | Improving Transformer World Models for Data-Efficient RL. Antoine Dedieu, Joseph Ortiz, Xinghua Lou, Carter Wendelken, J. Swaroop Guntupalli, Wolfgang Lehrach, Miguel Lázaro-Gredilla, Kevin Patrick Murphy |
| 2025 | Improving Value Estimation Critically Enhances Vanilla Policy Gradient. Tao Wang, Ruipeng Zhang, Sicun Gao |
| 2025 | Improving Your Model Ranking on Chatbot Arena by Vote Rigging. Rui Min, Tianyu Pang, Chao Du, Qian Liu, Minhao Cheng, Min Lin |
| 2025 | Improving Zero-Shot Adversarial Robustness in Vision-Language Models by Closed-form Alignment of Adversarial Path Simplices. Junhao Dong, Piotr Koniusz, Yifei Zhang, Hao Zhu, Weiming Liu, Xinghua Qu, Yew-Soon Ong |
| 2025 | Improving the Continuity of Goal-Achievement Ability via Policy Self-Regularization for Goal-Conditioned Reinforcement Learning. Xudong Gong, Sen Yang, Dawei Feng, Kele Xu, Bo Ding, Huaimin Wang, Yong Dou |
| 2025 | Improving the Diffusability of Autoencoders. Ivan Skorokhodov, Sharath Girish, Benran Hu, Willi Menapace, Yanyu Li, Rameen Abdal, Sergey Tulyakov, Aliaksandr Siarohin |
| 2025 | Improving the Effective Receptive Field of Message-Passing Neural Networks. Shahaf E. Finder, Ron Shapira Weber, Moshe Eliasof, Oren Freifeld, Eran Treister |
| 2025 | Improving the Scaling Laws of Synthetic Data with Deliberate Practice. Reyhane Askari Hemmat, Mohammad Pezeshki, Elvis Dohmatob, Florian Bordes, Pietro Astolfi, Melissa Hall, Jakob Verbeek, Michal Drozdzal, Adriana Romero-Soriano |
| 2025 | Improving the Statistical Efficiency of Cross-Conformal Prediction. Matteo Gasparin, Aaditya Ramdas |
| 2025 | Improving the Variance of Differentially Private Randomized Experiments through Clustering. Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie |
| 2025 | In-Context Adaptation to Concept Drift for Learned Database Operations. Jiaqi Zhu, Shaofeng Cai, Yanyan Shen, Gang Chen, Fang Deng, Beng Chin Ooi |
| 2025 | In-Context Deep Learning via Transformer Models. Weimin Wu, Maojiang Su, Jerry Yao-Chieh Hu, Zhao Song, Han Liu |
| 2025 | In-Context Denoising with One-Layer Transformers: Connections between Attention and Associative Memory Retrieval. Matthew Smart, Alberto Bietti, Anirvan M. Sengupta |
| 2025 | In-Context Fine-Tuning for Time-Series Foundation Models. Matthew Faw, Rajat Sen, Yichen Zhou, Abhimanyu Das |
| 2025 | In-Context Learning and Occam's Razor. Eric Elmoznino, Tom Marty, Tejas Kasetty, Léo Gagnon, Sarthak Mittal, Mahan Fathi, Dhanya Sridhar, Guillaume Lajoie |
| 2025 | In-Context Learning as Conditioned Associative Memory Retrieval. Weimin Wu, Teng-Yun Hsiao, Jerry Yao-Chieh Hu, Wenxin Zhang, Han Liu |
| 2025 | In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention. Jianliang He, Xintian Pan, Siyu Chen, Zhuoran Yang |
| 2025 | In-Context Reinforcement Learning From Suboptimal Historical Data. Juncheng Dong, Moyang Guo, Ethan X. Fang, Zhuoran Yang, Vahid Tarokh |
| 2025 | Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games. Tong Yang, Bo Dai, Lin Xiao, Yuejie Chi |
| 2025 | Incorporating Arbitrary Matrix Group Equivariance into KANs. Lexiang Hu, Yisen Wang, Zhouchen Lin |
| 2025 | Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems. Yujun Kim, Jaeyoung Cha, Chulhee Yun |
| 2025 | Independence Tests for Language Models. Sally Zhu, Ahmed M. Ahmed, Rohith Kuditipudi, Percy Liang |
| 2025 | Inducing, Detecting and Characterising Neural Modules: A Pipeline for Functional Interpretability in Reinforcement Learning. Anna Soligo, Pietro Ferraro, David Boyle |
| 2025 | Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations. Kexuan Shi, Hai Chen, Leheng Zhang, Shuhang Gu |
| 2025 | Inductive Moment Matching. Linqi Zhou, Stefano Ermon, Jiaming Song |
| 2025 | InfAlign: Inference-aware language model alignment. Ananth Balashankar, Ziteng Sun, Jonathan Berant, Jacob Eisenstein, Michael Collins, Adrian Hutter, Jong Lee, Chirag Nagpal, Flavien Prost, Aradhana Sinha, Ananda Theertha Suresh, Ahmad Beirami |
| 2025 | Inference-Time Alignment of Diffusion Models with Direct Noise Optimization. Zhiwei Tang, Jiangweizhi Peng, Jiasheng Tang, Mingyi Hong, Fan Wang, Tsung-Hui Chang |
| 2025 | Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language Models. Patrick Leask, Neel Nanda, Noura Al Moubayed |
| 2025 | Info-Coevolution: An Efficient Framework for Data Model Coevolution. Ziheng Qin, Hailun Xu, Wei Chee Yew, Qi Jia, Yang Luo, Kanchan Sarkar, Danhui Guan, Kai Wang, Yang You |
| 2025 | InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theory. Feifei Li, Mi Zhang, Zhaoxiang Wang, Min Yang |
| 2025 | InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective. Yuanhong Zhang, Muyao Yuan, Weizhan Zhang, Tieliang Gong, Wen Wen, Jiangyong Ying, Weijie Shi |
| 2025 | InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference. Tianyu Cui, Song-Jun Xu, Artem Moskalev, Shuwei Li, Tommaso Mansi, Mangal Prakash, Rui Liao |
| 2025 | Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting. Min Chen, Guansong Pang, Wenjun Wang, Cheng Yan |
| 2025 | Instance Correlation Graph-based Naive Bayes. Chengyuan Li, Liangxiao Jiang, Wenjun Zhang, Liangjun Yu, Huan Zhang |
| 2025 | Instance-Optimal Pure Exploration for Linear Bandits on Continuous Arms. Sho Takemori, Yuhei Umeda, Aditya Gopalan |
| 2025 | Instruct2See: Learning to Remove Any Obstructions Across Distributions. Junhang Li, Yu Guo, Chuhua Xian, Shengfeng He |
| 2025 | Instruction-Following Pruning for Large Language Models. Bairu Hou, Qibin Chen, Jianyu Wang, Guoli Yin, Chong Wang, Nan Du, Ruoming Pang, Shiyu Chang, Tao Lei |
| 2025 | IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models. Hang Guo, Yawei Li, Tao Dai, Shu-Tao Xia, Luca Benini |
| 2025 | Integer Programming for Generalized Causal Bootstrap Designs. Jennifer Rogers Brennan, Sébastien Lahaie, Adel Javanmard, Nick Doudchenko, Jean Pouget-Abadie |
| 2025 | Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models. Yang Zheng, Wen Li, Zhaoqiang Liu |
| 2025 | Integration-free Kernels for Equivariant Gaussian Process Modelling. Tim Steinert, David Ginsbourger, August Lykke-Møller, Ove Christiansen, Henry Moss |
| 2025 | Interaction-Aware Gaussian Weighting for Clustered Federated Learning. Alessandro Licciardi, Davide Leo, Eros Fanì, Barbara Caputo, Marco Ciccone |
| 2025 | Interchangeable Token Embeddings for Extendable Vocabulary and Alpha-Equivalence. Ilker Isik, Ramazan Gokberk Cinbis, Ebru Aydin Gol |
| 2025 | Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors. Jing Huang, Junyi Tao, Thomas Icard, Diyi Yang, Christopher Potts |
| 2025 | Interpolating Neural Network-Tensor Decomposition (INN-TD): a scalable and interpretable approach for large-scale physics-based problems. Jiachen Guo, Xiaoyu Xie, Chanwook Park, Hantao Zhang, Matthew Politis, Gino Domel, Wing Kam Liu |
| 2025 | Interpreting CLIP with Hierarchical Sparse Autoencoders. Vladimir Zaigrajew, Hubert Baniecki, Przemyslaw Biecek |
| 2025 | Interpreting the Repeated Token Phenomenon in Large Language Models. Itay Yona, Ilia Shumailov, Jamie Hayes, Yossi Gandelsman |
| 2025 | Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces. Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell |
| 2025 | Introducing 3D Representation for Dense Volume-to-Volume Translation via Score Fusion. Xiyue Zhu, Dou Hoon Kwark, Ruike Zhu, Kaiwen Hong, Yiqi Tao, Shirui Luo, Yudu Li, Zhi-Pei Liang, Volodymyr V. Kindratenko |
| 2025 | Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning. Changsheng Wang, Yihua Zhang, Jinghan Jia, Parikshit Ram, Dennis Wei, Yuguang Yao, Soumyadeep Pal, Nathalie Baracaldo, Sijia Liu |
| 2025 | Invariant Deep Uplift Modeling for Incentive Assignment in Online Marketing via Probability of Necessity and Sufficiency. Zexu Sun, Qiyu Han, Hao Yang, Anpeng Wu, Minqin Zhu, Dugang Liu, Chen Ma, Yunpeng Weng, Xing Tang, Xiuqiang He |
| 2025 | Inverse Bridge Matching Distillation. Nikita Gushchin, David Li, Daniil Selikhanovych, Evgeny Burnaev, Dmitry Baranchuk, Alexander Korotin |
| 2025 | Inverse Flow and Consistency Models. Yuchen Zhang, Jian Zhou |
| 2025 | Inverse Optimization via Learning Feasible Regions. Ke Ren, Peyman Mohajerin Esfahani, Angelos Georghiou |
| 2025 | Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo. Idan Achituve, Hai Victor Habi, Amir Rosenfeld, Arnon Netzer, Idit Diamant, Ethan Fetaya |
| 2025 | Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors. Jingyang Ke, Feiyang Wu, Jiyi Wang, Jeffrey Markowitz, Anqi Wu |
| 2025 | Inverse problems with experiment-guided AlphaFold. Sai Advaith Maddipatla, Nadav Bojan Sellam, Meital Bojan, Sanketh Vedula, Paul Schanda, Ailie Marx, Alexander M. Bronstein |
| 2025 | Investigating Non-Transitivity in LLM-as-a-Judge. Yi Xu, Laura Ruis, Tim Rocktäschel, Robert Kirk |
| 2025 | Investigating the Overlooked Hessian Structure: From CNNs to LLMs. Qian-Yuan Tang, Yufei Gu, Yunfeng Cai, Mingming Sun, Ping Li, Zhou Xun, Zeke Xie |
| 2025 | Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment. Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Akshay Krishnamurthy, Dylan J. Foster |
| 2025 | Is Complex Query Answering Really Complex? Cosimo Gregucci, Bo Xiong, Daniel Hernández, Lorenzo Loconte, Pasquale Minervini, Steffen Staab, Antonio Vergari |
| 2025 | Is Noise Conditioning Necessary for Denoising Generative Models? Qiao Sun, Zhicheng Jiang, Hanhong Zhao, Kaiming He |
| 2025 | Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs. Yinong Oliver Wang, Nivedha Sivakumar, Falaah Arif Khan, Katherine Metcalf, Adam Golinski, Natalie Mackraz, Barry-John Theobald, Luca Zappella, Nicholas Apostoloff |
| 2025 | Isolated Causal Effects of Natural Language. Victoria Lin, Louis-Philippe Morency, Eli Ben-Michael |
| 2025 | It's My Data Too: Private ML for Datasets with Multi-User Training Examples. Arun Ganesh, Ryan McKenna, Hugh Brendan McMahan, Adam Smith, Fan Wu |
| 2025 | Iterative Vectors: In-Context Gradient Steering without Backpropagation. Yiting Liu, Zhi-Hong Deng |
| 2025 | Jacobian Sparse Autoencoders: Sparsify Computations, Not Just Activations. Lucy Farnik, Tim Lawson, Conor J. Houghton, Laurence Aitchison |
| 2025 | Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning. Lang Pu, Jingjing Gu, Chao Lin, Xinyi Huang |
| 2025 | Joint Learning of Energy-based Models and their Partition Function. Michael Eli Sander, Vincent Roulet, Tianlin Liu, Mathieu Blondel |
| 2025 | Joint Localization and Activation Editing for Low-Resource Fine-Tuning. Wen Lai, Alexander Fraser, Ivan Titov |
| 2025 | Joint Metric Space Embedding by Unbalanced Optimal Transport with Gromov-Wasserstein Marginal Penalization. Florian Beier, Moritz Piening, Robert Beinert, Gabriele Steidl |
| 2025 | Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient. Jan Ludziejewski, Maciej Pióro, Jakub Krajewski, Maciej Stefaniak, Michal Krutul, Jan Malasnicki, Marek Cygan, Piotr Sankowski, Kamil Adamczewski, Piotr Milos, Sebastian Jaszczur |
| 2025 | Joker: Joint Optimization Framework for Lightweight Kernel Machines. Junhong Zhang, Zhihui Lai |
| 2025 | Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning. Mahavir Dabas, Si Chen, Charles Fleming, Ming Jin, Ruoxi Jia |
| 2025 | K2IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes. Hideaki Kim, Tomoharu Iwata, Akinori Fujino |
| 2025 | K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting. Xingjian Wu, Xiangfei Qiu, Hongfan Gao, Jilin Hu, Bin Yang, Chenjuan Guo |
| 2025 | KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems. Jusheng Zhang, Zimeng Huang, Yijia Fan, Ningyuan Liu, Mingyan Li, Zhuojie Yang, Jiawei Yao, Jian Wang, Keze Wang |
| 2025 | KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks. Quan Zhou, Changhua Pei, Fei Sun, Jing Han, Zhengwei Gao, Haiming Zhang, Gaogang Xie, Dan Pei, Jianhui Li |
| 2025 | KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search. Haoran Luo, Haihong E, Yikai Guo, Qika Lin, Xiaobao Wu, Xinyu Mu, Wenhao Liu, Meina Song, Yifan Zhu, Anh Tuan Luu |
| 2025 | KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies. Shih-Min Yang, Martin Magnusson, Johannes A. Stork, Todor Stoyanov |
| 2025 | KGMark: A Diffusion Watermark for Knowledge Graphs. Hongrui Peng, Haolang Lu, Yuanlong Yu, Weiye Fu, Kun Wang, Guoshun Nan |
| 2025 | KIND: Knowledge Integration and Diversion for Training Decomposable Models. Yucheng Xie, Fu Feng, Ruixiao Shi, Jing Wang, Yong Rui, Xin Geng |
| 2025 | KV Shifting Attention Enhances Language Modeling. Mingyu Xu, Bingning Wang, Weipeng Chen |
| 2025 | KVTuner: Sensitivity-Aware Layer-Wise Mixed-Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference. Xing Li, Zeyu Xing, Yiming Li, Linping Qu, Hui-Ling Zhen, Yiwu Yao, Wulong Liu, Sinno Jialin Pan, Mingxuan Yuan |
| 2025 | Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage. Konstantina Bairaktari, Jiayun Wu, Steven Wu |
| 2025 | Kernel Quantile Embeddings and Associated Probability Metrics. Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol, Krikamol Muandet |
| 2025 | Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models. Shizhan Gong, Yankai Jiang, Qi Dou, Farzan Farnia |
| 2025 | KernelBench: Can LLMs Write Efficient GPU Kernels? Anne Ouyang, Simon Guo, Simran Arora, Alex L. Zhang, William Hu, Christopher Ré, Azalia Mirhoseini |
| 2025 | KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors. Benson Chen, Tomasz Danel, Gabriel H. S. Dreiman, Patrick J. McEnaney, Nikhil Jain, Kirill Novikov, Spurti Umesh Akki, Joshua L. Turnbull, Virja Atul Pandya, Boris P. Belotserkovskii, Jared Bryce Weaver, Ankita Biswas, Dat Nguyen, Kent Gorday, Mohammad Sultan, Nathaniel Stanley, Daniel M. Whalen, Divya Kanichar, Christoph Klein, Emily Fox, R. Edward Watts |
| 2025 | Kinetic Langevin Diffusion for Crystalline Materials Generation. François R. J. Cornet, Federico Bergamin, Arghya Bhowmik, Juan Maria Garcia Lastra, Jes Frellsen, Mikkel N. Schmidt |
| 2025 | Knowledge Retention in Continual Model-Based Reinforcement Learning. Haotian Fu, Yixiang Sun, Michael Littman, George Konidaris |
| 2025 | Knowledge Swapping via Learning and Unlearning. Mingyu Xing, Lechao Cheng, Shengeng Tang, Yaxiong Wang, Zhun Zhong, Meng Wang |
| 2025 | Knowledge-Guided Wasserstein Distributionally Robust Optimization. Zitao Wang, Ziyuan Wang, Molei Liu, Nian Si |
| 2025 | KoNODE: Koopman-Driven Neural Ordinary Differential Equations with Evolving Parameters for Time Series Analysis. Hanru Bai, Weiyang Ding |
| 2025 | Kona: An Efficient Privacy-Preservation Framework for KNN Classification by Communication Optimization. Guopeng Lin, Ruisheng Zhou, Shuyu Chen, Weili Han, Jin Tan, Wenjing Fang, Lei Wang, Tao Wei |
| 2025 | KoopSTD: Reliable Similarity Analysis between Dynamical Systems via Approximating Koopman Spectrum with Timescale Decoupling. Shimin Zhang, Ziyuan Ye, Yinsong Yan, Zeyang Song, Yujie Wu, Jibin Wu |
| 2025 | L-Diffusion: Laplace Diffusion for Efficient Pathology Image Segmentation. Weihan Li, Linyun Zhou, Yang Jian, Shengxuming Zhang, Xiangtong Du, Xiuming Zhang, Jing Zhang, Chaoqing Xu, Mingli Song, Zunlei Feng |
| 2025 | L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning. Xiang Zhang, Run He, Chen Jiao, Di Fang, Ming Li, Ziqian Zeng, Cen Chen, Huiping Zhuang |
| 2025 | LADA: Scalable Label-Specific CLIP Adapter for Continual Learning. Mao-Lin Luo, Zi-Hao Zhou, Tong Wei, Min-Ling Zhang |
| 2025 | LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models. Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann |
| 2025 | LARM: Large Auto-Regressive Model for Long-Horizon Embodied Intelligence. Zhuoling Li, Xiaogang Xu, Zhenhua Xu, Ser-Nam Lim, Hengshuang Zhao |
| 2025 | LASER: Attention with Exponential Transformation. Sai Surya Duvvuri, Inderjit S. Dhillon |
| 2025 | LAST SToP for Modeling Asynchronous Time Series. Shubham Gupta, Thibaut Durand, Graham W. Taylor, Lilian W. Bialokozowicz |
| 2025 | LAuReL: Learned Augmented Residual Layer. Gaurav Menghani, Ravi Kumar, Sanjiv Kumar |
| 2025 | LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation. Li Ding, Hao Zhang, Wenrui Dai, Chenglin Li, Weijia Lu, Zhifei Yang, Xiaodong Zhang, Xiaofeng Ma, Junni Zou, Hongkai Xiong |
| 2025 | LDMol: A Text-to-Molecule Diffusion Model with Structurally Informative Latent Space Surpasses AR Models. Jinho Chang, Jong Chul Ye |
| 2025 | LEAPS: A discrete neural sampler via locally equivariant networks. Peter Holderrieth, Michael Samuel Albergo, Tommi S. Jaakkola |
| 2025 | LEMoN: Label Error Detection using Multimodal Neighbors. Haoran Zhang, Aparna Balagopalan, Nassim Oufattole, Hyewon Jeong, Yan Wu, Jiacheng Zhu, Marzyeh Ghassemi |
| 2025 | LETS Forecast: Learning Embedology for Time Series Forecasting. Abrar Majeedi, Viswanatha Reddy Gajjala, Satya Sai Srinath Namburi GNVV, Nada Magdi Elkordi, Yin Li |
| 2025 | LEVIS: Large Exact Verifiable Input Spaces for Neural Networks. Mohamad Fares El Hajj Chehade, Wenting Li, Brian Wesley Bell, Russell Bent, Saif R. Kazi, Hao Zhu |
| 2025 | LGDM: Latent Guidance in Diffusion Models for Perceptual Evaluations. Shreshth Saini, Ru-Ling Liao, Yan Ye, Alan Bovik |
| 2025 | LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning. Zihang Liu, Tianyu Pang, Oleg Balabanov, Chaoqun Yang, Tianjin Huang, Lu Yin, Yaoqing Yang, Shiwei Liu |
| 2025 | LIMEFLDL: A Local Interpretable Model-Agnostic Explanations Approach for Label Distribution Learning. Xiuyi Jia, Jinchi Li, Yunan Lu, Weiwei Li |
| 2025 | LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces. Rashid Mushkani, Shravan Nayak, Hugo Berard, Allison Cohen, Shin Koseki, Hadrien Bertrand |
| 2025 | LLM Alignment as Retriever Optimization: An Information Retrieval Perspective. Bowen Jin, Jinsung Yoon, Zhen Qin, Ziqi Wang, Wei Xiong, Yu Meng, Jiawei Han, Sercan Ö. Arik |
| 2025 | LLM Data Selection and Utilization via Dynamic Bi-level Optimization. Yang Yu, Kai Han, Hang Zhou, Yehui Tang, Kaiqi Huang, Yunhe Wang, Dacheng Tao |
| 2025 | LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification. Hang Gao, Wenxuan Huang, Fengge Wu, Junsuo Zhao, Changwen Zheng, Huaping Liu |
| 2025 | LLM-Assisted Semantically Diverse Teammate Generation for Efficient Multi-agent Coordination. Lihe Li, Lei Yuan, Pengsen Liu, Tao Jiang, Yang Yu |
| 2025 | LLM-Augmented Chemical Synthesis and Design Decision Programs. Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller, Yuanqi Du, Chao Zhang |
| 2025 | LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models. Parshin Shojaee, Ngoc-Hieu Nguyen, Kazem Meidani, Amir Barati Farimani, Khoa D. Doan, Chandan K. Reddy |
| 2025 | LLMScan: Causal Scan for LLM Misbehavior Detection. Mengdi Zhang, Kai Kiat Goh, Peixin Zhang, Jun Sun, Lin Xin Rose, Hongyu Zhang |
| 2025 | LLMs Can Reason Faster Only If We Let Them. Bilgehan Sel, Lifu Huang, Naren Ramakrishnan, Ruoxi Jia, Ming Jin |
| 2025 | LLMs can see and hear without any training. Kumar Ashutosh, Yossi Gandelsman, Xinlei Chen, Ishan Misra, Rohit Girdhar |
| 2025 | LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws. Prasanna Mayilvahanan, Thaddäus Wiedemer, Sayak Mallick, Matthias Bethge, Wieland Brendel |
| 2025 | LLaVA-ReID: Selective Multi-image Questioner for Interactive Person Re-Identification. Yiding Lu, Mouxing Yang, Dezhong Peng, Peng Hu, Yijie Lin, Xi Peng |
| 2025 | LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations. Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, Tim Genewein |
| 2025 | LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models. Marwa Abdulhai, Isadora White, Charlie Victor Snell, Charles Sun, Joey Hong, Yuexiang Zhai, Kelvin Xu, Sergey Levine |
| 2025 | LOB-Bench: Benchmarking Generative AI for Finance - an Application to Limit Order Book Data. Peer Nagy, Sascha Yves Frey, Kang Li, Bidipta Sarkar, Svitlana Vyetrenko, Stefan Zohren, Ani Calinescu, Jakob Nicolaus Foerster |
| 2025 | LOCATE 3D: Real-World Object Localization via Self-Supervised Learning in 3D. Paul McVay, Sergio Arnaud, Ada Martin, Arjun Majumdar, Krishna Murthy Jatavallabhula, Phillip Thomas, Ruslan Partsey, Daniel Dugas, Abha Gejji, Alexander Sax, Vincent-Pierre Berges, Mikael Henaff, Ayush Jain, Ang Cao, Ishita Prasad, Mrinal Kalakrishnan, Michael Rabbat, Nicolas Ballas, Mido Assran, Oleksandr Maksymets, Aravind Rajeswaran |
| 2025 | LOGO - Long cOntext aliGnment via efficient preference Optimization. Zecheng Tang, Zechen Sun, Juntao Li, Qiaoming Zhu, Min Zhang |
| 2025 | LRA-QViT: Integrating Low-Rank Approximation and Quantization for Robust and Efficient Vision Transformers. Beom Jin Kang, Nam Joon Kim, Hyun Kim |
| 2025 | LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation. Elizabeth Fons, Alejandro Sztrajman, Yousef El-Laham, Luciana Ferrer, Svitlana Vyetrenko, Manuela Veloso |
| 2025 | LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models. Tzu-Tao Chang, Shivaram Venkataraman |
| 2025 | La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse Activation. Kai Liu, Bowen Xu, Shaoyu Wu, Xin Chen, Hao Zhou, Yongliang Tao, Lulu Hu |
| 2025 | LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models. Dachuan Shi, Yonggan Fu, Xiangchi Yuan, Zhongzhi Yu, Haoran You, Sixu Li, Xin Dong, Jan Kautz, Pavlo Molchanov, Yingyan Celine Lin |
| 2025 | LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation. Chen-Chia Chang, Wan-Hsuan Lin, Yikang Shen, Yiran Chen, Xin Zhang |
| 2025 | LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs - No Silver Bullet for LC or RAG Routing. Kuan Li, Liwen Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Shuai Wang, Minhao Cheng |
| 2025 | Label Distribution Propagation-based Label Completion for Crowdsourcing. Tong Wu, Liangxiao Jiang, Wenjun Zhang, Chaoqun Li |
| 2025 | Ladder-Residual: Parallelism-Aware Architecture for Accelerating Large Model Inference with Communication Overlapping. Muru Zhang, Mayank Mishra, Zhongzhu Zhou, William Brandon, Jue Wang, Yoon Kim, Jonathan Ragan-Kelley, Shuaiwen Leon Song, Ben Athiwaratkun, Tri Dao |
| 2025 | LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation. Piyush Tiwary, Kinjawl Bhattacharyya, Prathosh AP |
| 2025 | LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization. Wenzhe Niu, Zongxia Xie, Yanru Sun, Wei He, Man Xu, Chao Hao |
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| 2025 | Large Language Models are Demonstration Pre-Selectors for Themselves. Jiarui Jin, Yuwei Wu, Haoxuan Li, Xiaoting He, Weinan Zhang, Yiming Yang, Yong Yu, Jun Wang, Mengyue Yang |
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| 2025 | Learning With Multi-Group Guarantees For Clusterable Subpopulations. Jessica Dai, Nika Haghtalab, Eric Zhao |
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| 2025 | Learning dynamics in linear recurrent neural networks. Alexandra Maria Proca, Clémentine Carla Juliette Dominé, Murray Shanahan, Pedro A. M. Mediano |
| 2025 | Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning. Lianbo Ma, Jianlun Ma, Yuee Zhou, Guoyang Xie, Qiang He, Zhichao Lu |
| 2025 | Learning from Sample Stability for Deep Clustering. Zhixin Li, Yuheng Jia, Hui Liu, Junhui Hou |
| 2025 | Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network. Jijia Liu, Feng Gao, Qingmin Liao, Chao Yu, Yu Wang |
| 2025 | Learning from True-False Labels via Multi-modal Prompt Retrieving. Zhongnian Li, Jinghao Xu, Peng Ying, Meng Wei, Xinzheng Xu |
| 2025 | Learning from others' mistakes: Finetuning machine translation models with span-level error annotations. Lily H. Zhang, Hamid Dadkhahi, Mara Finkelstein, Firas Trabelsi, Jiaming Luo, Markus Freitag |
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| 2025 | Learning to Generate Projections for Reducing Dimensionality of Heterogeneous Linear Programming Problems. Tomoharu Iwata, Shinsaku Sakaue |
| 2025 | Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals. Junyan Liu, Arnab Maiti, Artin Tajdini, Kevin Jamieson, Lillian J. Ratliff |
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| 2025 | Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge. Swarnadeep Saha, Xian Li, Marjan Ghazvininejad, Jason E. Weston, Tianlu Wang |
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| 2025 | Learning to Reuse Policies in State Evolvable Environments. Ziqian Zhang, Bohan Yang, Lihe Li, Yuqi Bian, Ruiqi Xue, Feng Chen, Yi-Chen Li, Lei Yuan, Yang Yu |
| 2025 | Learning to Route LLMs with Confidence Tokens. Yu-Neng Chuang, Prathusha Kameswara Sarma, Parikshit Gopalan, John Boccio, Sara Bolouki, Xia Hu, Helen Zhou |
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| 2025 | Learning to Stop: Deep Learning for Mean Field Optimal Stopping. Lorenzo Magnino, Yuchen Zhu, Mathieu Laurière |
| 2025 | Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL. Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang, Sheng-Jun Huang |
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| 2025 | LongRoPE2: Near-Lossless LLM Context Window Scaling. Ning Shang, Li Lyna Zhang, Siyuan Wang, Gaokai Zhang, Gilsinia Lopez, Fan Yang, Weizhu Chen, Mao Yang |
| 2025 | LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding. Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao, Lemeng Wu, Jun Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Balakrishnan Varadarajan, Florian Bordes, Zhuang Liu, Hu Xu, Hyunwoo J. Kim, Bilge Soran, Raghuraman Krishnamoorthi, Mohamed Elhoseiny, Vikas Chandra |
| 2025 | Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models. Xin Zou, Yizhou Wang, Yibo Yan, Yuanhuiyi Lyu, Kening Zheng, Sirui Huang, Junkai Chen, Peijie Jiang, Jia Liu, Chang Tang, Xuming Hu |
| 2025 | Looking Beyond the Top-1: Transformers Determine Top Tokens in Order. Daria Lioubashevski, Tomer Schlank, Gabriel Stanovsky, Ariel Goldstein |
| 2025 | Loss Functions and Operators Generated by f-Divergences. Vincent Roulet, Tianlin Liu, Nino Vieillard, Michael Eli Sander, Mathieu Blondel |
| 2025 | LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression. Haotian Wu, Gongpu Chen, Pier Luigi Dragotti, Deniz Gündüz |
| 2025 | Low-Dimension-to-High-Dimension Generalization and Its Implications for Length Generalization. Yang Chen, Long Yang, Yitao Liang, Zhouchen Lin |
| 2025 | Low-Rank Adapting Models for Sparse Autoencoders. Matthew Chen, Joshua Engels, Max Tegmark |
| 2025 | Low-Rank Tensor Transitions (LoRT) for Transferable Tensor Regression. Andong Wang, Yuning Qiu, Zhong Jin, Guoxu Zhou, Qibin Zhao |
| 2025 | Low-Rank Thinning. Annabelle Michael Carrell, Albert Gong, Abhishek Shetty, Raaz Dwivedi, Lester Mackey |
| 2025 | Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space. Max van Spengler, Pascal Mettes |
| 2025 | LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits. Zikai Zhou, Qizheng Zhang, Hermann Kumbong, Kunle Olukotun |
| 2025 | Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers. Alireza Amiri Bavandpour, Xinting Huang, Mark Rofin, Michael Hahn |
| 2025 | M+: Extending MemoryLLM with Scalable Long-Term Memory. Yu Wang, Dmitry Krotov, Yuanzhe Hu, Yifan Gao, Wangchunshu Zhou, Julian J. McAuley, Dan Gutfreund, Rogério Feris, Zexue He |
| 2025 | M2PDE: Compositional Generative Multiphysics and Multi-component PDE Simulation. Tao Zhang, Zhenhai Liu, Feipeng Qi, Yongjun Jiao, Tailin Wu |
| 2025 | M3-JEPA: Multimodal Alignment via Multi-gate MoE based on the Joint-Embedding Predictive Architecture. Hongyang Lei, Xiaolong Cheng, Qi Qin, Dan Wang, Huazhen Huang, Qingqing Gu, Yetao Wu, Luo Ji |
| 2025 | MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem Proving. Ruida Wang, Rui Pan, Yuxin Li, Jipeng Zhang, Yizhen Jia, Shizhe Diao, Renjie Pi, Junjie Hu, Tong Zhang |
| 2025 | MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces. Loris Gaven, Thomas Carta, Clément Romac, Cédric Colas, Sylvain Lamprier, Olivier Sigaud, Pierre-Yves Oudeyer |
| 2025 | MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning. Zihan Chen, Song Wang, Zhen Tan, Jundong Li, Cong Shen |
| 2025 | MARGE: Improving Math Reasoning with Guided Exploration. Jingyue Gao, Runji Lin, Keming Lu, Bowen Yu, Junyang Lin, Jianyu Chen |
| 2025 | MARS: Unleashing the Power of Variance Reduction for Training Large Models. Huizhuo Yuan, Yifeng Liu, Shuang Wu, Xun Zhou, Quanquan Gu |
| 2025 | MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems. Rui Ye, Shuo Tang, Rui Ge, Yaxin Du, Zhenfei Yin, Siheng Chen, Jing Shao |
| 2025 | MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models. Jiazheng Li, Lu Yu, Qing Cui, Zhiqiang Zhang, Jun Zhou, Yanfang Ye, Chuxu Zhang |
| 2025 | MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations. Kaixuan Huang, Jiacheng Guo, Zihao Li, Xiang Ji, Jiawei Ge, Wenzhe Li, Yingqing Guo, Tianle Cai, Hui Yuan, Runzhe Wang, Yue Wu, Ming Yin, Shange Tang, Yangsibo Huang, Chi Jin, Xinyun Chen, Chiyuan Zhang, Mengdi Wang |
| 2025 | MATS: An Audio Language Model under Text-only Supervision. Wen Wang, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen |
| 2025 | MCU: An Evaluation Framework for Open-Ended Game Agents. Xinyue Zheng, Haowei Lin, Kaichen He, Zihao Wang, Qiang Fu, Haobo Fu, Zilong Zheng, Yitao Liang |
| 2025 | MDDM: Practical Message-Driven Generative Image Steganography Based on Diffusion Models. Zihao Xu, Dawei Xu, Zihan Li, Chuan Zhang |
| 2025 | MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents. Kaijie Zhu, Xianjun Yang, Jindong Wang, Wenbo Guo, William Yang Wang |
| 2025 | MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning. Suning Huang, Zheyu Aqa Zhang, Tianhai Liang, Yihan Xu, Zhehao Kou, Chenhao Lu, Guowei Xu, Zhengrong Xue, Huazhe Xu |
| 2025 | MERGE3: Efficient Evolutionary Merging on Consumer-grade GPUs. Tommaso Mencattini, Adrian Robert Minut, Donato Crisostomi, Andrea Santilli, Emanuele Rodolà |
| 2025 | MERIT: Maximum-normalized Element-wise Ratio for Language Model Large-batch Training. Yang Luo, Zangwei Zheng, Ziheng Qin, Zirui Zhu, Yong Liu, Yang You |
| 2025 | MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning. Peter Eckmann, Dongxia Wu, Germano Heinzelmann, Michael K. Gilson, Rose Yu |
| 2025 | MGD3 : Mode-Guided Dataset Distillation using Diffusion Models. Jeffrey A. Chan-Santiago, Praveen Tirupattur, Gaurav Kumar Nayak, Gaowen Liu, Mubarak Shah |
| 2025 | MIB: A Mechanistic Interpretability Benchmark. Aaron Mueller, Atticus Geiger, Sarah Wiegreffe, Dana Arad, Iván Arcuschin, Adam Belfki, Yik Siu Chan, Jaden Fried Fiotto-Kaufman, Tal Haklay, Michael Hanna, Jing Huang, Rohan Gupta, Yaniv Nikankin, Hadas Orgad, Nikhil Prakash, Anja Reusch, Aruna Sankaranarayanan, Shun Shao, Alessandro Stolfo, Martin Tutek, Amir Zur, David Bau, Yonatan Belinkov |
| 2025 | MIPT: Multilevel Informed Prompt Tuning for Robust Molecular Property Prediction. Yeyun Chen, Jiangming Shi |
| 2025 | MIRROR: Make Your Object-Level Multi-View Generation More Consistent with Training-Free Rectification. Tianchi Xing, Bonan Li, Congying Han, Xinmin Qiu, Zicheng Zhang, Tiande Guo |
| 2025 | ML2-GCL: Manifold Learning Inspired Lightweight Graph Contrastive Learning. Jianqing Liang, Zhiqiang Li, Xinkai Wei, Yuan Liu, Zhiqiang Wang |
| 2025 | MM-RLHF: The Next Step Forward in Multimodal LLM Alignment. Yifan Zhang, Tao Yu, Haochen Tian, Chaoyou Fu, Peiyan Li, Jianshu Zeng, Wulin Xie, Yang Shi, Huanyu Zhang, Junkang Wu, Xue Wang, Yibo Hu, Bin Wen, Tingting Gao, Zhang Zhang, Fan Yang, Di Zhang, Liang Wang, Rong Jin |
| 2025 | MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency. Dongzhi Jiang, Renrui Zhang, Ziyu Guo, Yanwei Li, Yu Qi, Xinyan Chen, Liuhui Wang, Jianhan Jin, Claire Guo, Shen Yan, Bo Zhang, Chaoyou Fu, Peng Gao, Hongsheng Li |
| 2025 | MMInference: Accelerating Pre-filling for Long-Context Visual Language Models via Modality-Aware Permutation Sparse Attention. Yucheng Li, Huiqiang Jiang, Chengruidong Zhang, Qianhui Wu, Xufang Luo, Surin Ahn, Amir H. Abdi, Dongsheng Li, Jianfeng Gao, Yuqing Yang, Lili Qiu |
| 2025 | MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization. Kangyu Zhu, Peng Xia, Yun Li, Hongtu Zhu, Sheng Wang, Huaxiu Yao |
| 2025 | MODA: MOdular Duplex Attention for Multimodal Perception, Cognition, and Emotion Understanding. Zhicheng Zhang, Wuyou Xia, Chenxi Zhao, Zhou Yan, Xiaoqiang Liu, Yongjie Zhu, Wenyu Qin, Pengfei Wan, Di Zhang, Jufeng Yang |
| 2025 | MODULI: Unlocking Preference Generalization via Diffusion Models for Offline Multi-Objective Reinforcement Learning. Yifu Yuan, Zhenrui Zheng, Zibin Dong, Jianye Hao |
| 2025 | MOGIC: Metadata-infused Oracle Guidance for Improved Extreme Classification. Suchith Chidananda Prabhu, Bhavyajeet Singh, Anshul Mittal, Siddarth Asokan, Shikhar Mohan, Deepak Saini, Yashoteja Prabhu, Lakshya Kumar, Jian Jiao, Amit Singh, Niket Tandon, Manish Gupta, Sumeet Agarwal, Manik Varma |
| 2025 | MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking. Sebastian Farquhar, Vikrant Varma, David Lindner, David Elson, Caleb Biddulph, Ian Goodfellow, Rohin Shah |
| 2025 | MP-Nav: Enhancing Data Poisoning Attacks against Multimodal Learning. Jingfeng Zhang, Prashanth Krishnamurthy, Naman Patel, Anthony Tzes, Farshad Khorrami |
| 2025 | MPO: An Efficient Post-Processing Framework for Mixing Diverse Preference Alignment. Tianze Wang, Dongnan Gui, Yifan Hu, Shuhang Lin, Linjun Zhang |
| 2025 | MTL-UE: Learning to Learn Nothing for Multi-Task Learning. Yi Yu, Song Xia, Siyuan Yang, Chenqi Kong, Wenhan Yang, Shijian Lu, Yap-Peng Tan, Alex C. Kot |
| 2025 | MTSTRec: Multimodal Time-Aligned Shared Token Recommender. Ming-Yi Hong, Yen-Jung Hsu, Miao-Chen Chiang, Che Lin |
| 2025 | MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections. Da Xiao, Qingye Meng, Shengping Li, Xingyuan Yuan |
| 2025 | MVA: Linear Attention with High-order Query-Keys Integration and Multi-level Vocabulary Decomposition. Ning Wang, Zekun Li, Tongxin Bai, Man Yao, Zhen Qin, Guoqi Li |
| 2025 | Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics. Herman Chau, Helen Jenne, Davis Brown, Jesse He, Mark Raugas, Sara C. Billey, Henry Kvinge |
| 2025 | Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes. Jesse He, Helen Jenne, Herman Chau, Davis Brown, Mark Raugas, Sara C. Billey, Henry Kvinge |
| 2025 | Mahalanobis++: Improving OOD Detection via Feature Normalization. Maximilian Müller, Matthias Hein |
| 2025 | Maintaining Proportional Committees with Dynamic Candidate Sets. Chris Dong, Jannik Peters |
| 2025 | Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment. Chenghao Fan, Zhenyi Lu, Sichen Liu, Chengfeng Gu, Xiaoye Qu, Wei Wei, Yu Cheng |
| 2025 | Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic. Eshika Saxena, Alberto Alfarano, Emily Wenger, Kristin E. Lauter |
| 2025 | MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models. Mahir Labib Dihan, Md Tanvir Hassan, Md Tanvir Parvez, Md Hasebul Hasan, Md Almash Alam, Muhammad Aamir Cheema, Mohammed Eunus Ali, Md. Rizwan Parvez |
| 2025 | Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More. Xialie Zhuang, Zhikai Jia, Jianjin Li, Zhenyu Zhang, Li Shen, Zheng Cao, Shiwei Liu |
| 2025 | MaskTwins: Dual-form Complementary Masking for Domain-Adaptive Image Segmentation. Jiawen Wang, Yinda Chen, Xiaoyu Liu, Che Liu, Dong Liu, Jianqing Gao, Zhiwei Xiong |
| 2025 | Masked Autoencoders Are Effective Tokenizers for Diffusion Models. Hao Chen, Yujin Han, Fangyi Chen, Xiang Li, Yidong Wang, Jindong Wang, Ze Wang, Zicheng Liu, Difan Zou, Bhiksha Raj |
| 2025 | Masked Generative Nested Transformers with Decode Time Scaling. Sahil Goyal, Debapriya Tula, Gagan Jain, Pradeep Shenoy, Prateek Jain, Sujoy Paul |
| 2025 | Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding. Mingyu Jin, Kai Mei, Wujiang Xu, Mingjie Sun, Ruixiang Tang, Mengnan Du, Zirui Liu, Yongfeng Zhang |
| 2025 | Mastering Board Games by External and Internal Planning with Language Models. John Schultz, Jakub Adámek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada A. Lewis, Anian Ruoss, Tom Zahavy, Petar Velickovic, Laurel Prince, Satinder Singh, Eric Malmi, Nenad Tomasev |
| 2025 | Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer. Yilun Kong, Guozheng Ma, Qi Zhao, Haoyu Wang, Li Shen, Xueqian Wang, Dacheng Tao |
| 2025 | Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defer. Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2025 | MathConstruct: Challenging LLM Reasoning with Constructive Proofs. Mislav Balunovic, Jasper Dekoninck, Nikola Jovanovic, Ivo Petrov, Martin T. Vechev |
| 2025 | Matrix Completion with Incomplete Side Information via Orthogonal Complement Projection. Gengshuo Chang, Wei Zhang, Lehan Zhang |
| 2025 | Matryoshka Quantization. Pranav Ajit Nair, Puranjay Datta, Jeff Dean, Prateek Jain, Aditya Kusupati |
| 2025 | Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators. Shanda Li, Shinjae Yoo, Yiming Yang |
| 2025 | Maximizing Intermediate Checkpoint Value in LLM Pretraining with Bayesian Optimization. Deyuan Liu, Zecheng Wang, Bingning Wang, Weipeng Chen, Chunshan Li, Zhiying Tu, Dianhui Chu, Dianbo Sui |
| 2025 | Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures. Alina Ene, Alessandro Epasto, Vahab Mirrokni, Hoai-An Nguyen, Huy L. Nguyen, David P. Woodruff, Peilin Zhong |
| 2025 | Maximum Entropy Reinforcement Learning with Diffusion Policy. Xiaoyi Dong, Jian Cheng, Xi Sheryl Zhang |
| 2025 | Maximum Total Correlation Reinforcement Learning. Bang You, Puze Liu, Huaping Liu, Jan Peters, Oleg Arenz |
| 2025 | Measuring Diversity in Synthetic Datasets. Yuchang Zhu, Huizhe Zhang, Bingzhe Wu, Jintang Li, Zibin Zheng, Peilin Zhao, Liang Chen, Yatao Bian |
| 2025 | Measuring Diversity: Axioms and Challenges. Mikhail Mironov, Liudmila Prokhorenkova |
| 2025 | Measuring In-Context Computation Complexity via Hidden State Prediction. Vincent Herrmann, Róbert Csordás, Jürgen Schmidhuber |
| 2025 | Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective. Joonkyu Kim, Yejin Kim, Jy-yong Sohn |
| 2025 | Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence. Joseph Paillard, Angel David Reyero Lobo, Vitaliy Kolodyazhniy, Bertrand Thirion, Denis-Alexander Engemann |
| 2025 | Mechanisms of Projective Composition of Diffusion Models. Arwen Bradley, Preetum Nakkiran, David Berthelot, James Thornton, Joshua M. Susskind |
| 2025 | Mechanistic PDE Networks for Discovery of Governing Equations. Adeel Pervez, Efstratios Gavves, Francesco Locatello |
| 2025 | Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization. Phillip Guo, Aaquib Syed, Abhay Sheshadri, Aidan Ewart, Gintare Karolina Dziugaite |
| 2025 | MedRAX: Medical Reasoning Agent for Chest X-ray. Adibvafa Fallahpour, Jun Ma, Alif Munim, Hongwei Lyu, Bo Wang |
| 2025 | MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding. Yuxin Zuo, Shang Qu, Yifei Li, Zhang-Ren Chen, Xuekai Zhu, Ermo Hua, Kaiyan Zhang, Ning Ding, Bowen Zhou |
| 2025 | MemFreezing: A Novel Adversarial Attack on Temporal Graph Neural Networks under Limited Future Knowledge. Yue Dai, Liang Liu, Xulong Tang, Youtao Zhang, Jun Yang |
| 2025 | Memorization Sinks: Isolating Memorization during LLM Training. Gaurav Rohit Ghosal, Pratyush Maini, Aditi Raghunathan |
| 2025 | Memory Layers at Scale. Vincent-Pierre Berges, Barlas Oguz, Daniel Haziza, Wen-tau Yih, Luke Zettlemoyer, Gargi Ghosh |
| 2025 | Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation. Juncheol Shin, Minsang Seok, Seonggon Kim, Eunhyeok Park |
| 2025 | Meta Optimality for Demographic Parity Constrained Regression via Post-Processing. Kazuto Fukuchi |
| 2025 | Meta-Black-Box-Optimization through Offline Q-function Learning. Zeyuan Ma, Zhiguang Cao, Zhou Jiang, Hongshu Guo, Yue-Jiao Gong |
| 2025 | Meta-Reinforcement Learning with Adaptation from Human Feedback via Preference-Order-Preserving Task Embedding. Siyuan Xu, Minghui Zhu |
| 2025 | MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines. Yaolun Zhang, Xiaogeng Liu, Chaowei Xiao |
| 2025 | MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters. Arsalan Sharifnassab, Saber Salehkaleybar, Richard S. Sutton |
| 2025 | Metadata Conditioning Accelerates Language Model Pre-training. Tianyu Gao, Alexander Wettig, Luxi He, Yihe Dong, Sadhika Malladi, Danqi Chen |
| 2025 | Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation. Juno Kim, Denny Wu, Jason D. Lee, Taiji Suzuki |
| 2025 | MetricEmbedding: Accelerate Metric Nearness by Tropical Inner Product. Muyang Cao, Jiajun Yu, Xin Du, Gang Pan, Wei Wang |
| 2025 | MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance. Yuang Zhang, Jiaxi Gu, Li-Wen Wang, Han Wang, Junqi Cheng, Yuefeng Zhu, Fangyuan Zou |
| 2025 | Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse. Ryan Liu, Jiayi Geng, Addison J. Wu, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths |
| 2025 | Mind the Gap: A Practical Attack on GGUF Quantization. Kazuki Egashira, Robin Staab, Mark Vero, Jingxuan He, Martin T. Vechev |
| 2025 | Mind the Gap: a Spectral Analysis of Rank Collapse and Signal Propagation in Attention Layers. Thiziri Nait Saada, Alireza Naderi, Jared Tanner |
| 2025 | MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data. Yuqin Dai, Zhouheng Yao, Chunfeng Song, Qihao Zheng, Weijian Mai, Kunyu Peng, Shuai Lu, Wanli Ouyang, Jian Yang, Jiamin Wu |
| 2025 | MindCustomer: Multi-Context Image Generation Blended with Brain Signal. Muzhou Yu, Shuyun Lin, Lei Ma, Bo Lei, Kaisheng Ma |
| 2025 | MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-text Decoding. Weikang Qiu, Zheng Huang, Haoyu Hu, Aosong Feng, Yujun Yan, Rex Ying |
| 2025 | Minerva: A Programmable Memory Test Benchmark for Language Models. Menglin Xia, Victor Rühle, Saravan Rajmohan, Reza Shokri |
| 2025 | Minimalist Concept Erasure in Generative Models. Yang Zhang, Er Jin, Yanfei Dong, Yixuan Wu, Philip Torr, Ashkan Khakzar, Johannes Stegmaier, Kenji Kawaguchi |
| 2025 | Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback. Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon Shaolei Du, Ruosong Wang |
| 2025 | Minimum Width for Universal Approximation using Squashable Activation Functions. Jonghyun Shin, Namjun Kim, Geonho Hwang, Sejun Park |
| 2025 | MiraGe: Editable 2D Images using Gaussian Splatting. Joanna Waczynska, Tomasz Szczepanik, Piotr Borycki, Slawomir Konrad Tadeja, Thomas Bohné, Przemyslaw Spurek |
| 2025 | Mirror, Mirror of the Flow: How Does Regularization Shape Implicit Bias? Tom Jacobs, Chao Zhou, Rebekka Burkholz |
| 2025 | MissScore: High-Order Score Estimation in the Presence of Missing Data. Wenqin Liu, Haoze Hou, Erdun Gao, Biwei Huang, Qiuhong Ke, Howard D. Bondell, Mingming Gong |
| 2025 | Mitigating Heterogeneous Token Overfitting in LLM Knowledge Editing. Tianci Liu, Ruirui Li, Zihan Dong, Hui Liu, Xianfeng Tang, Qingyu Yin, Linjun Zhang, Haoyu Wang, Jing Gao |
| 2025 | Mitigating Local Cohesion and Global Sparseness in Graph Contrastive Learning with Fuzzy Boundaries. Yuena Lin, Haichun Cai, Jun-Yi Hang, Haobo Wang, Zhen Yang, Gengyu Lyu |
| 2025 | Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance. Linxi Zhao, Yihe Deng, Weitong Zhang, Quanquan Gu |
| 2025 | Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification. Langzhang Liang, Fanchen Bu, Zixing Song, Zenglin Xu, Shirui Pan, Kijung Shin |
| 2025 | Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn. Hongyao Tang, Johan S. Obando-Ceron, Pablo Samuel Castro, Aaron C. Courville, Glen Berseth |
| 2025 | Mitigating over-Exploration in Latent Space Optimization using les. Omer Ronen, Ahmed Imtiaz Humayun, Richard G. Baraniuk, Randall Balestriero, Bin Yu |
| 2025 | MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger Bridges. Shixi Qin, Zhiyong Yang, Shilong Bao, Shi Wang, Qianqian Xu, Qingming Huang |
| 2025 | MixMin: Finding Data Mixtures via Convex Minimization. Anvith Thudi, Evianne Rovers, Yangjun Ruan, Tristan Thrush, Chris J. Maddison |
| 2025 | Mixed-curvature decision trees and random forests. Philippe Chlenski, Quentin Chu, Raiyan R. Khan, Kaizhu Du, Antonio Khalil Moretti, Itsik Pe'er |
| 2025 | Mixture of Experts Made Intrinsically Interpretable. Xingyi Yang, Constantin Venhoff, Ashkan Khakzar, Christian Schröder de Witt, Puneet K. Dokania, Adel Bibi, Philip Torr |
| 2025 | Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning. Ryotaro Kawata, Kohsei Matsutani, Yuri Kinoshita, Naoki Nishikawa, Taiji Suzuki |
| 2025 | Mixture of Hidden-Dimensions: Not All Hidden-States' Dimensions are Needed in Transformer. Yilong Chen, Junyuan Shang, Zhenyu Zhang, Jiawei Sheng, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang |
| 2025 | Mixture of Lookup Experts. Shibo Jie, Yehui Tang, Kai Han, Yitong Li, Duyu Tang, Zhi-Hong Deng, Yunhe Wang |
| 2025 | MoE-SVD: Structured Mixture-of-Experts LLMs Compression via Singular Value Decomposition. Wei Li, Lujun Li, Hao Gu, You-Liang Huang, Mark G. Lee, Shengjie Sun, Wei Xue, Yike Guo |
| 2025 | MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance. Zhixuan Chen, Xing Hu, Dawei Yang, Zukang Xu, Chen Xu, Zhihang Yuan, Sifan Zhou, Jiangyong Yu |
| 2025 | MoH: Multi-Head Attention as Mixture-of-Head Attention. Peng Jin, Bo Zhu, Li Yuan, Shuicheng Yan |
| 2025 | MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition. Sungnyun Kim, Kangwook Jang, Sangmin Bae, Sungwoo Cho, Se-Young Yun |
| 2025 | MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition. Yuhuan Yang, Chaofan Ma, Zhenjie Mao, Jiangchao Yao, Ya Zhang, Yanfeng Wang |
| 2025 | MoRAgent: Parameter Efficient Agent Tuning with Mixture-of-Roles. Jing Han, Binwei Yan, Tianyu Guo, Zheyuan Bai, Mengyu Zheng, Hanting Chen, Ying Nie |
| 2025 | Modalities Contribute Unequally: Enhancing Medical Multi-modal Learning through Adaptive Modality Token Re-balancing. Jie Peng, Jenna L. Ballard, Mohan Zhang, Sukwon Yun, Jiayi Xin, Qi Long, Yanyong Zhang, Tianlong Chen |
| 2025 | Model Immunization from a Condition Number Perspective. Amber Yijia Zheng, Site Bai, Brian Bullins, Raymond A. Yeh |
| 2025 | Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws. Xiyuan Wei, Ming Lin, Fanjiang Ye, Fengguang Song, Liangliang Cao, My T. Thai, Tianbao Yang |
| 2025 | Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence. Shangbin Feng, Zifeng Wang, Yike Wang, Sayna Ebrahimi, Hamid Palangi, Lesly Miculicich, Achin Kulshrestha, Nathalie Rauschmayr, Yejin Choi, Yulia Tsvetkov, Chen-Yu Lee, Tomas Pfister |
| 2025 | Model Uncertainty Quantification by Conformal Prediction in Continual Learning. Rui Gao, Weiwei Liu |
| 2025 | Model-Based Exploration in Monitored Markov Decision Processes. Alireza Kazemipour, Matthew E. Taylor, Michael Bowling |
| 2025 | Modeling All-Atom Glycan Structures via Hierarchical Message Passing and Multi-Scale Pre-training. Minghao Xu, Jiaze Song, Keming Wu, Xiangxin Zhou, Bin Cui, Wentao Zhang |
| 2025 | Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent. Yongxian Wei, Anke Tang, Li Shen, Zixuan Hu, Chun Yuan, Xiaochun Cao |
| 2025 | Models of Heavy-Tailed Mechanistic Universality. Liam Hodgkinson, Zhichao Wang, Michael W. Mahoney |
| 2025 | Modified K-means Algorithm with Local Optimality Guarantees. Mingyi Li, Michael R. Metel, Akiko Takeda |
| 2025 | Modular Duality in Deep Learning. Jeremy Bernstein, Laker Newhouse |
| 2025 | Modularized Self-Reflected Video Reasoner for Multimodal LLM with Application to Video Question Answering. Zihan Song, Xin Wang, Zi Qian, Hong Chen, Longtao Huang, Hui Xue, Wenwu Zhu |
| 2025 | Modulated Diffusion: Accelerating Generative Modeling with Modulated Quantization. Weizhi Gao, Zhichao Hou, Junqi Yin, Feiyi Wang, Linyu Peng, Xiaorui Liu |
| 2025 | Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts. Xu Liu, Juncheng Liu, Gerald Woo, Taha Aksu, Yuxuan Liang, Roger Zimmermann, Chenghao Liu, Junnan Li, Silvio Savarese, Caiming Xiong, Doyen Sahoo |
| 2025 | Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning. Wenjing Yan, Xiangyu Zhong, Xiaolu Wang, Ying-Jun Angela Zhang |
| 2025 | Monte Carlo Tree Diffusion for System 2 Planning. Jaesik Yoon, Hyeonseo Cho, Doojin Baek, Yoshua Bengio, Sungjin Ahn |
| 2025 | Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design. Zhi Zheng, Zhuoliang Xie, Zhenkun Wang, Bryan Hooi |
| 2025 | Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport. Tuan Dam, Pascal Stenger, Lukas Schneider, Joni Pajarinen, Carlo D'Eramo, Odalric-Ambrym Maillard |
| 2025 | More Than Meets the Eye: Enhancing Multi-Object Tracking Even with Prolonged Occlusions. Bishoy Galoaa, Somaieh Amraee, Sarah Ostadabbas |
| 2025 | Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models. Chao Li, Jiawei Fan, Anbang Yao |
| 2025 | MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible Cost. Sen Xing, Muyan Zhong, Zeqiang Lai, Liangchen Li, Jiawen Liu, Yaohui Wang, Jifeng Dai, Wenhai Wang |
| 2025 | Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits. Su Jia, Peter I. Frazier, Nathan Kallus |
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| 2025 | Multi-Marginal Stochastic Flow Matching for High-Dimensional Snapshot Data at Irregular Time Points. Justin Lee, Behnaz Moradijamei, Heman Shakeri |
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| 2025 | Multi-Objective Causal Bayesian Optimization. Shriya Bhatija, Paul-David Joshua Zuercher, Jakob Thumm, Thomas Bohné |
| 2025 | Multi-Session Budget Optimization for Forward Auction-based Federated Learning. Xiaoli Tang, Han Yu, Zengxiang Li, Xiaoxiao Li |
| 2025 | Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning. Adrià López Escoriza, Nicklas Hansen, Stone Tao, Tongzhou Mu, Hao Su |
| 2025 | Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks. Rohit Sonker, Alexandre Capone, Andrew Rothstein, Hiro Josep Farre Kaga, Egemen Kolemen, Jeff Schneider |
| 2025 | Multi-Turn Code Generation Through Single-Step Rewards. Arnav Kumar Jain, Gonzalo Gonzalez-Pumariega, Wayne Chen, Alexander M. Rush, Wenting Zhao, Sanjiban Choudhury |
| 2025 | Multi-View Graph Clustering via Node-Guided Contrastive Encoding. Yazhou Ren, Junlong Ke, Zichen Wen, Tianyi Wu, Yang Yang, Xiaorong Pu, Lifang He |
| 2025 | Multi-agent Architecture Search via Agentic Supernet. Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, Xiang Wang |
| 2025 | Multi-band Frequency Reconstruction for Neural Psychoacoustic Coding. Dianwen Ng, Kun Zhou, Yi-Wen Chao, Zhiwei Xiong, Bin Ma, Engsiong Chng |
| 2025 | Multi-objective Linear Reinforcement Learning with Lexicographic Rewards. Bo Xue, Dake Bu, Ji Cheng, Yuanyu Wan, Qingfu Zhang |
| 2025 | MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation. Qi Wang, Yuan Mi, Haoyun Wang, Yi Zhang, Ruizhi Chengze, Hongsheng Liu, Ji-Rong Wen, Hao Sun |
| 2025 | Multiaccuracy and Multicalibration via Proxy Groups. Beepul Bharti, Mary Versa Clemens-Sewall, Paul H. Yi, Jeremias Sulam |
| 2025 | Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion. Dohoon Lee, Jaehyun Park, Hyunwoo J. Kim, Kyogu Lee |
| 2025 | Multilayer Matrix Factorization via Dimension-Reducing Diffusion Variational Inference. Junbin Liu, Farzan Farnia, Wing-Kin Ma |
| 2025 | Multimodal Medical Code Tokenizer. Xiaorui Su, Shvat Messica, Yepeng Huang, Ruth Johnson, Lukas Fesser, Shanghua Gao, Faryad Sahneh, Marinka Zitnik |
| 2025 | Multinoulli Extension: A Lossless Yet Effective Probabilistic Framework for Subset Selection over Partition Constraints. Qixin Zhang, Wei Huang, Can Jin, Puning Zhao, Yao Shu, Li Shen, Dacheng Tao |
| 2025 | Multiobjective distribution matching. Xiaoyuan Zhang, Peijie Li, Yingying Yu, Yichi Zhang, Han Zhao, Qingfu Zhang |
| 2025 | Multiple-policy Evaluation via Density Estimation. Yilei Chen, Aldo Pacchiano, Ioannis Paschalidis |
| 2025 | Multivariate Conformal Selection. Tian Bai, Yue Zhao, Xiang Yu, Archer Y. Yang |
| 2025 | MuseControlLite: Multifunctional Music Generation with Lightweight Conditioners. Fang-Duo Tsai, Shih-Lun Wu, Weijaw Lee, Sheng-Ping Yang, Bo-Rui Chen, Hao-Chung Cheng, Yi-Hsuan Yang |
| 2025 | Mutual Learning for SAM Adaptation: A Dual Collaborative Network Framework for Source-Free Domain Transfer. Yabo Liu, Waikeung Wong, Chengliang Liu, Xiaoling Luo, Yong Xu, Jinghua Wang |
| 2025 | MxMoE: Mixed-precision Quantization for MoE with Accuracy and Performance Co-Design. Haojie Duanmu, Xiuhong Li, Zhihang Yuan, Size Zheng, Jiangfei Duan, Xingcheng Zhang, Dahua Lin |
| 2025 | M³HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality. Ziyan Wang, Zhicheng Zhang, Fei Fang, Yali Du |
| 2025 | N2GON: Neural Networks for Graph-of-Net with Position Awareness. Yejiang Wang, Yuhai Zhao, Zhengkui Wang, Wen Shan, Ling Li, Qian Li, Miaomiao Huang, Meixia Wang, Shirui Pan, Xingwei Wang |
| 2025 | NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations. Myunsoo Kim, Hayeong Lee, Seong-Woong Shim, JunHo Seo, Byung-Jun Lee |
| 2025 | NEAR: Neural Electromagnetic Array Response. Yinyan Bu, Jiajie Yu, Kai Zheng, Xinyu Zhang, Piya Pal |
| 2025 | NETS: A Non-equilibrium Transport Sampler. Michael Samuel Albergo, Eric Vanden-Eijnden |
| 2025 | NExtLong: Toward Effective Long-Context Training without Long Documents. Chaochen Gao, Xing Wu, Zijia Lin, Debing Zhang, Songlin Hu |
| 2025 | NICE Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric. Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Pang Wei Koh, Chuan-Sheng Foo, Bryan Kian Hsiang Low |
| 2025 | NMA-tune: Generating Highly Designable and Dynamics Aware Protein Backbones. Urszula Julia Komorowska, Francisco Vargas, Alessandro Rondina, Pietro Lio, Mateja Jamnik |
| 2025 | NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel. Gabriel Thompson, Kai Yue, Chau-Wai Wong, Huaiyu Dai |
| 2025 | NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction. Qichao Wang, Ziqiao Meng, Wenqian Cui, Yifei Zhang, Pengcheng Wu, Bingzhe Wu, Irwin King, Liang Chen, Peilin Zhao |
| 2025 | Natural Perturbations for Black-box Training of Neural Networks by Zeroth-Order Optimization. Hiroshi Sawada, Kazuo Aoyama, Yuya Hikima |
| 2025 | Navigating Conflicting Views: Harnessing Trust for Learning. Jueqing Lu, Wray L. Buntine, Yuanyuan Qi, Joanna Dipnall, Belinda Gabbe, Lan Du |
| 2025 | Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning. Fangwen Wu, Lechao Cheng, Shengeng Tang, Xiaofeng Zhu, Chaowei Fang, Dingwen Zhang, Meng Wang |
| 2025 | Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning. Cheol Woo Kim, Jai Moondra, Shresth Verma, Madeleine Pollack, Lingkai Kong, Milind Tambe, Swati Gupta |
| 2025 | Near Optimal Best Arm Identification for Clustered Bandits. Yash, Avishek Ghosh, Nikhil Karamchandani |
| 2025 | Near Optimal Non-asymptotic Sample Complexity of 1-Identification. Zitian Li, Wang Chi Cheung |
| 2025 | Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems. Mohammadreza Daneshvaramoli, Helia Karisani, Adam Lechowicz, Bo Sun, Cameron Musco, Mohammad Hajiesmaili |
| 2025 | Near-Optimal Decision Trees in a SPLIT Second. Varun Babbar, Hayden McTavish, Cynthia Rudin, Margo I. Seltzer |
| 2025 | Near-Optimal Sample Complexity for MDPs via Anchoring. Jongmin Lee, Mario Bravo, Roberto Cominetti |
| 2025 | Near-optimal Regret Using Policy Optimization in Online MDPs with Aggregate Bandit Feedback. Tal Lancewicki, Yishay Mansour |
| 2025 | Near-optimal Sketchy Natural Gradients for Physics-Informed Neural Networks. Maricela Best McKay, Avleen Kaur, Chen Greif, Brian Wetton |
| 2025 | Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback. Qiwei Di, Jiafan He, Quanquan Gu |
| 2025 | Nearly Optimal Sample Complexity for Learning with Label Proportions. Róbert Istvan Busa-Fekete, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren, Uri Stemmer |
| 2025 | NegMerge: Sign-Consensual Weight Merging for Machine Unlearning. Hyoseo Kim, Dongyoon Han, Junsuk Choe |
| 2025 | Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling. Xinxing Shi, Xiaoyu Jiang, Mauricio A. Álvarez |
| 2025 | Nemotron-CORTEXA: Enhancing LLM Agents for Software Engineering Tasks via Improved Localization and Solution Diversity. Atefeh Sohrabizadeh, Jialin Song, Mingjie Liu, Rajarshi Roy, Chankyu Lee, Jonathan Raiman, Bryan Catanzaro |
| 2025 | NestQuant: nested lattice quantization for matrix products and LLMs. Semyon Savkin, Eitan Porat, Or Ordentlich, Yury Polyanskiy |
| 2025 | Nested Expectations with Kernel Quadrature. Zonghao Chen, Masha Naslidnyk, François-Xavier Briol |
| 2025 | Nesterov Method for Asynchronous Pipeline Parallel Optimization. Thalaiyasingam Ajanthan, Sameera Ramasinghe, Yan Zuo, Gil Avraham, Alexander Long |
| 2025 | Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning. Guozheng Ma, Lu Li, Zilin Wang, Li Shen, Pierre-Luc Bacon, Dacheng Tao |
| 2025 | Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime. Diyuan Wu, Marco Mondelli |
| 2025 | Neural Discovery in Mathematics: Do Machines Dream of Colored Planes? Konrad Mundinger, Max Zimmer, Aldo Kiem, Christoph Spiegel, Sebastian Pokutta |
| 2025 | Neural Encoding and Decoding at Scale. Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre, Zixuan Wang, Hanrui Lyu, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole Lincoln Hurwitz |
| 2025 | Neural Event-Triggered Control with Optimal Scheduling. Luan Yang, Jingdong Zhang, Qunxi Zhu, Wei Lin |
| 2025 | Neural Genetic Search in Discrete Spaces. Hyeonah Kim, Sanghyeok Choi, Jiwoo Son, Jinkyoo Park, Changhyun Kwon |
| 2025 | Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning. Runzhong Wang, Rui-Xi Wang, Mrunali Manjrekar, Connor W. Coley |
| 2025 | Neural Guided Diffusion Bridges. Gefan Yang, Frank van der Meulen, Stefan Sommer |
| 2025 | Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery. Ning Liu, Yue Yu |
| 2025 | Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment. Yu Zhu, Chunfeng Song, Wanli Ouyang, Shan Yu, Tiejun Huang |
| 2025 | Neural Solver Selection for Combinatorial Optimization. Chengrui Gao, Haopu Shang, Ke Xue, Chao Qian |
| 2025 | NeuralCohort: Cohort-aware Neural Representation Learning for Healthcare Analytics. Changshuo Liu, Lingze Zeng, Kaiping Zheng, Shaofeng Cai, Beng Chin Ooi, James Wei Luen Yip |
| 2025 | NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders. Jun-En Ding, Dongsheng Luo, Chenwei Wu, Feng Liu |
| 2025 | NeuronTune: Towards Self-Guided Spurious Bias Mitigation. Guangtao Zheng, Wenqian Ye, Aidong Zhang |
| 2025 | Neurosymbolic World Models for Sequential Decision Making. Leonardo Hernandez Cano, Maxine Perroni-Scharf, Neil Dhir, Arun Ramamurthy, Armando Solar-Lezama |
| 2025 | Neutral residues: revisiting adapters for model extension. Franck Signe Talla, Edouard Grave, Hervé Jégou |
| 2025 | New Bounds for Sparse Variational Gaussian Processes. Michalis K. Titsias |
| 2025 | NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits. Tushar Aggarwal, Swayam Singh, Abhijeet Awasthi, Aditya Kanade, Nagarajan Natarajan |
| 2025 | No Free Lunch from Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions. Benjamin S. Ruben, William Lingxiao Tong, Hamza Tahir Chaudhry, Cengiz Pehlevan |
| 2025 | No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets. Corinna Coupette, Jeremy Wayland, Emily Simons, Bastian Rieck |
| 2025 | No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks. Attila Szász, Balázs Bánhelyi, Márk Jelasity |
| 2025 | No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces. Daniel Marczak, Simone Magistri, Sebastian Cygert, Bartlomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer |
| 2025 | No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization. Martino Bernasconi, Matteo Castiglioni, Andrea Celli |
| 2025 | NoLiMa: Long-Context Evaluation Beyond Literal Matching. Ali Modarressi, Hanieh Deilamsalehy, Franck Dernoncourt, Trung Bui, Ryan A. Rossi, Seunghyun Yoon, Hinrich Schütze |
| 2025 | Noise Conditional Variational Score Distillation. Xinyu Peng, Ziyang Zheng, Yaoming Wang, Han Li, Nuowen Kan, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong |
| 2025 | Noise-Guided Predicate Representation Extraction and Diffusion-Enhanced Discretization for Scene Graph Generation. Guoqing Zhang, Shichao Kan, Fanghui Zhang, Wanru Xu, Yue Zhang, Yigang Cen |
| 2025 | Noisy SIGNSGD Is More Differentially Private Than You (Might) Think. Richeng Jin, Huaiyu Dai |
| 2025 | Non-Asymptotic Length Generalization. Thomas Chen, Tengyu Ma, Zhiyuan Li |
| 2025 | Non-Asymptotic and Non-Lipschitzian Bounds on Optimal Values in Stochastic Optimization Under Heavy Tails. Jindong Tong, Hongcheng Liu, Johannes O. Royset |
| 2025 | Non-Stationary Predictions May Be More Informative: Exploring Pseudo-Labels with a Two-Phase Pattern of Training Dynamics. Hongbin Pei, Jingxin Hai, Yu Li, Huiqi Deng, Denghao Ma, Jie Ma, Pinghui Wang, Jing Tao, Xiaohong Guan |
| 2025 | Non-asymptotic Error Bounds in W2-Distance with Sqrt(d) Dimension Dependence and First Order Convergence for Langevin Monte Carlo beyond Log-Concavity. Bin Yang, Xiaojie Wang |
| 2025 | Non-stationary Diffusion For Probabilistic Time Series Forecasting. Weiwei Ye, Zhuopeng Xu, Ning Gui |
| 2025 | Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability. Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama |
| 2025 | Nonconvex Theory of M-estimators with Decomposable Regularizers. Weiwei Liu |
| 2025 | Nonlinear transformers can perform inference-time feature learning. Naoki Nishikawa, Yujin Song, Kazusato Oko, Denny Wu, Taiji Suzuki |
| 2025 | Nonlinearly Preconditioned Gradient Methods under Generalized Smoothness. Konstantinos A. Oikonomidis, Jan Quan, Emanuel Laude, Panagiotis Patrinos |
| 2025 | Nonparametric Identification of Latent Concepts. Yujia Zheng, Shaoan Xie, Kun Zhang |
| 2025 | Nonparametric Modern Hopfield Models. Jerry Yao-Chieh Hu, Bo-Yu Chen, Dennis Wu, Feng Ruan, Han Liu |
| 2025 | Nonparametric Teaching for Graph Property Learners. Chen Zhang, Weixin Bu, Zeyi Ren, Zhengwu Liu, Yik-Chung Wu, Ngai Wong |
| 2025 | Normalizing Flows are Capable Generative Models. Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran, David Berthelot, Jiatao Gu, Huangjie Zheng, Tianrong Chen, Miguel Ángel Bautista, Navdeep Jaitly, Joshua M. Susskind |
| 2025 | Not All Tokens Matter All The Time: Dynamic Token Aggregation Towards Efficient Detection Transformers. Jiacheng Cheng, Xiwen Yao, Xiang Yuan, Junwei Han |
| 2025 | Not All Wrong is Bad: Using Adversarial Examples for Unlearning. Ali Ebrahimpour Boroojeny, Hari Sundaram, Varun Chandrasekaran |
| 2025 | Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks. Lukas Braun, Erin Grant, Andrew M. Saxe |
| 2025 | Novelty Detection in Reinforcement Learning with World Models. Geigh Zollicoffer, Kenneth Eaton, Jonathan C. Balloch, Julia M. Kim, Wei Zhou, Robert Wright, Mark O. Riedl |
| 2025 | O-MAPL: Offline Multi-agent Preference Learning. The Viet Bui, Tien Anh Mai, Thanh Hong Nguyen |
| 2025 | OOD-Chameleon: Is Algorithm Selection for OOD Generalization Learnable? Liangze Jiang, Damien Teney |
| 2025 | OR-Bench: An Over-Refusal Benchmark for Large Language Models. Justin Cui, Wei-Lin Chiang, Ion Stoica, Cho-Jui Hsieh |
| 2025 | OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction. Huang Huang, Fangchen Liu, Letian Fu, Tingfan Wu, Mustafa Mukadam, Jitendra Malik, Ken Goldberg, Pieter Abbeel |
| 2025 | OV-MER: Towards Open-Vocabulary Multimodal Emotion Recognition. Zheng Lian, Haiyang Sun, Licai Sun, Haoyu Chen, Lan Chen, Hao Gu, Zhuofan Wen, Shun Chen, Siyuan Zhang, Hailiang Yao, Bin Liu, Rui Liu, Shan Liang, Ya Li, Jiangyan Yi, Jianhua Tao |
| 2025 | OW-VAP: Visual Attribute Parsing for Open World Object Detection. Xing Xi, Xing Fu, Weiqiang Wang, Ronghua Luo |
| 2025 | OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models. William Chen, Jinchuan Tian, Yifan Peng, Brian Yan, Chao-Han Huck Yang, Shinji Watanabe |
| 2025 | Objective drives the consistency of representational similarity across datasets. Laure Ciernik, Lorenz Linhardt, Marco Morik, Jonas Dippel, Simon Kornblith, Lukas Muttenthaler |
| 2025 | Observation Interference in Partially Observable Assistance Games. Scott Emmons, Caspar Oesterheld, Vincent Conitzer, Stuart Russell |
| 2025 | Occult: Optimizing Collaborative Communications across Experts for Accelerated Parallel MoE Training and Inference. Shuqing Luo, Pingzhi Li, Jie Peng, Yang Zhao, Yu Cao, Yu Cheng, Tianlong Chen |
| 2025 | Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents. Shuo Han, German Espinosa, Junda Huang, Daniel A. Dombeck, Malcolm A. MacIver, Bradly C. Stadie |
| 2025 | Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation. Kosuke Nakanishi, Akihiro Kubo, Yuji Yasui, Shin Ishii |
| 2025 | Off-Policy Evaluation under Nonignorable Missing Data. Han Wang, Yang Xu, Wenbin Lu, Rui Song |
| 2025 | Offline Learning for Combinatorial Multi-armed Bandits. Xutong Liu, Xiangxiang Dai, Jinhang Zuo, Siwei Wang, Carlee Joe-Wong, John C. S. Lui, Wei Chen |
| 2025 | Offline Model-based Optimization for Real-World Molecular Discovery. Dong-Hee Shin, Young-Han Son, Hyun Jung Lee, Deok-Joong Lee, Tae-Eui Kam |
| 2025 | Offline Opponent Modeling with Truncated Q-driven Instant Policy Refinement. Yuheng Jing, Kai Li, Bingyun Liu, Ziwen Zhang, Haobo Fu, Qiang Fu, Junliang Xing, Jian Cheng |
| 2025 | Offline-to-Online Reinforcement Learning with Classifier-Free Diffusion Generation. Xiao Huang, Xu Liu, Enze Zhang, Tong Yu, Shuai Li |
| 2025 | Olica: Efficient Structured Pruning of Large Language Models without Retraining. Jiujun He, Huazhen Lin |
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| 2025 | Omni-Angle Assault: An Invisible and Powerful Physical Adversarial Attack on Face Recognition. Shuai Yuan, Hongwei Li, Rui Zhang, Hangcheng Cao, Wenbo Jiang, Tao Ni, Wenshu Fan, Qingchuan Zhao, Guowen Xu |
| 2025 | OmniArch: Building Foundation Model for Scientific Computing. Tianyu Chen, Haoyi Zhou, Ying Li, Hao Wang, Chonghan Gao, Rongye Shi, Shanghang Zhang, Jianxin Li |
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| 2025 | On the Provable Separation of Scales in Maximal Update Parameterization. Letong Hong, Zhangyang Wang |
| 2025 | On the Query Complexity of Verifier-Assisted Language Generation. Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan T. Ash, Cyril Zhang, Andrej Risteski |
| 2025 | On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents. Jen-tse Huang, Jiaxu Zhou, Tailin Jin, Xuhui Zhou, Zixi Chen, Wenxuan Wang, Youliang Yuan, Michael R. Lyu, Maarten Sap |
| 2025 | On the Robustness of Reward Models for Language Model Alignment. Jiwoo Hong, Noah Lee, Eunki Kim, Guijin Son, Woojin Chung, Aman Gupta, Shao Tang, James Thorne |
| 2025 | On the Role of Label Noise in the Feature Learning Process. Andi Han, Wei Huang, Zhanpeng Zhou, Gang Niu, Wuyang Chen, Junchi Yan, Akiko Takeda, Taiji Suzuki |
| 2025 | On the Similarities of Embeddings in Contrastive Learning. Chungpa Lee, Sehee Lim, Kibok Lee, Jy-yong Sohn |
| 2025 | On the Statistical Mechanisms of Distributional Compositional Generalization. Jingwen Fu, Nanning Zheng |
| 2025 | On the Tension between Byzantine Robustness and No-Attack Accuracy in Distributed Learning. Yi-Rui Yang, Chang-Wei Shi, Wu-Jun Li |
| 2025 | On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures. Wei Shen, Ruida Zhou, Jing Yang, Cong Shen |
| 2025 | On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains. Xun Xian, Ganghua Wang, Xuan Bi, Rui Zhang, Jayanth Srinivasa, Ashish Kundu, Charles Fleming, Mingyi Hong, Jie Ding |
| 2025 | On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists. Dongyang Fan, Bettina Messmer, Nikita Doikov, Martin Jaggi |
| 2025 | On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention for Long-Context LLM Serving. Yeonju Ro, Zhenyu Zhang, Souvik Kundu, Zhangyang Wang, Aditya Akella |
| 2025 | One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory. Yuhang Li, Tong Liu, Yangguang Cui, Ming Hu, Xiaoqiang Li |
| 2025 | One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation. Jianze Li, Jiezhang Cao, Yong Guo, Wenbo Li, Yulun Zhang |
| 2025 | One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs. Yinghui Li, Jiayi Kuang, Haojing Huang, Zhikun Xu, Xinnian Liang, Yi Yu, Wenlian Lu, Yangning Li, Xiaoyu Tan, Chao Qu, Ying Shen, Hai-Tao Zheng, Philip S. Yu |
| 2025 | One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing Framework. Feiran Li, Qianqian Xu, Shilong Bao, Zhiyong Yang, Xiaochun Cao, Qingming Huang |
| 2025 | One Leaf Reveals the Season: Occlusion-Based Contrastive Learning with Semantic-Aware Views for Efficient Visual Representation. Xiaoyu Yang, Lijian Xu, Hongsheng Li, Shaoting Zhang |
| 2025 | One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy. Jiacheng Zhang, Benjamin I. P. Rubinstein, Jingfeng Zhang, Feng Liu |
| 2025 | One Wave To Explain Them All: A Unifying Perspective On Feature Attribution. Gabriel Kasmi, Amandine Brunetto, Thomas Fel, Jayneel Parekh |
| 2025 | One-Pass Feature Evolvable Learning with Theoretical Guarantees. Cun-Yuan Xing, Meng-Zhang Qian, Wuyang Chen, Wei Gao, Zhi-Hua Zhou |
| 2025 | One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models. Mingzhao Yang, Shangchao Su, Bin Li, Xiangyang Xue |
| 2025 | One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation. Zhendong Wang, Max Li, Ajay Mandlekar, Zhenjia Xu, Jiaojiao Fan, Yashraj Narang, Linxi Fan, Yuke Zhu, Yogesh Balaji, Mingyuan Zhou, Ming-Yu Liu, Yu Zeng |
| 2025 | One-Step Generalization Ratio Guided Optimization for Domain Generalization. Sumin Cho, Dongwon Kim, Kwangsu Kim |
| 2025 | One-dimensional Path Convolution. Xuanshu Luo, Martin Werner |
| 2025 | OneForecast: A Universal Framework for Global and Regional Weather Forecasting. Yuan Gao, Hao Wu, Ruiqi Shu, Huanshuo Dong, Fan Xu, Rui Ray Chen, Yibo Yan, Qingsong Wen, Xuming Hu, Kun Wang, Jiahao Wu, Qing Li, Hui Xiong, Xiaomeng Huang |
| 2025 | Online Clustering of Dueling Bandits. Zhiyong Wang, Jiahang Sun, Mingze Kong, Jize Xie, Qinghua Hu, John C. S. Lui, Zhongxiang Dai |
| 2025 | Online Conformal Prediction via Online Optimization. Felipe Areces, Christopher Mohri, Tatsunori Hashimoto, John C. Duchi |
| 2025 | Online Curvature-Aware Replay: Leveraging 2nd Order Information for Online Continual Learning. Edoardo Urettini, Antonio Carta |
| 2025 | Online Detection of LLM-Generated Texts via Sequential Hypothesis Testing by Betting. Can Chen, Jun-Kun Wang |
| 2025 | Online Differentially Private Conformal Prediction for Uncertainty Quantification. Qiangqiang Zhang, Ting Li, Xinwei Feng, Xiaodong Yan, Jinhan Xie |
| 2025 | Online Episodic Convex Reinforcement Learning. Bianca Marin Moreno, Khaled Eldowa, Pierre Gaillard, Margaux Brégère, Nadia Oudjane |
| 2025 | Online Laplacian-Based Representation Learning in Reinforcement Learning. Maheed H. Ahmed, Jayanth Bhargav, Mahsa Ghasemi |
| 2025 | Online Learning in Risk Sensitive constrained MDP. Arnob Ghosh, Mehrdad Moharrami |
| 2025 | Online Learning in the Random-Order Model. Martino Bernasconi, Andrea Celli, Riccardo Colini-Baldeschi, Federico Fusco, Stefano Leonardi, Matteo Russo |
| 2025 | Online Learning with Unknown Constraints. Karthik Sridharan, Seung Won Wilson Yoo |
| 2025 | Online Linear Classification with Massart Noise. Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2025 | Online Pre-Training for Offline-to-Online Reinforcement Learning. Yongjae Shin, Jeonghye Kim, Whiyoung Jung, Sunghoon Hong, Deunsol Yoon, Youngsoo Jang, Geon-Hyeong Kim, Jongseong Chae, Youngchul Sung, Kanghoon Lee, Woohyung Lim |
| 2025 | Online Robust Reinforcement Learning Through Monte-Carlo Planning. Tuan Dam, Kishan Panaganti, Brahim Driss, Adam Wierman |
| 2025 | Online Sparsification of Bipartite-Like Clusters in Graphs. Joyentanuj Das, Suranjan De, He Sun |
| 2025 | Open Materials Generation with Stochastic Interpolants. Philipp Höllmer, Thomas Egg, Maya M. Martirossyan, Eric Fuemmeler, Zeren Shui, Amit Gupta, Pawan Prakash, Adrian Roitberg, Mingjie Liu, George Karypis, Mark K. Transtrum, Richard G. Hennig, Ellad B. Tadmor, Stefano Martiniani |
| 2025 | Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction. Yanbin Wei, Xuehao Wang, Zhan Zhuang, Yang Chen, Shuhao Chen, Yulong Zhang, James T. Kwok, Yu Zhang |
| 2025 | Open-Det: An Efficient Learning Framework for Open-Ended Detection. Guiping Cao, Tao Wang, Wenjian Huang, Xiangyuan Lan, Jianguo Zhang, Dongmei Jiang |
| 2025 | OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning. Cong Hua, Qianqian Xu, Zhiyong Yang, Zitai Wang, Shilong Bao, Qingming Huang |
| 2025 | OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling. Hongliang Lu, Zhonglin Xie, Yaoyu Wu, Can Ren, Yuxuan Chen, Zaiwen Wen |
| 2025 | Optimal Algorithm for Max-Min Fair Bandit. Zilong Wang, Zhiyao Zhang, Shuai Li |
| 2025 | Optimal Auction Design in the Joint Advertising. Yang Li, Yuchao Ma, Qi Qi |
| 2025 | Optimal Decision Tree Pruning Revisited: Algorithms and Complexity. Juha Harviainen, Frank Sommer, Manuel Sorge, Stefan Szeider |
| 2025 | Optimal Fair Learning Robust to Adversarial Distribution Shift. Sushant Agarwal, Amit Deshpande, Rajmohan Rajaraman, Ravi Sundaram |
| 2025 | Optimal Information Retention for Time-Series Explanations. Jinghang Yue, Jing Wang, Lu Zhang, Shuo Zhang, Da Li, Zhaoyang Ma, Youfang Lin |
| 2025 | Optimal Sensor Scheduling and Selection for Continuous-Discrete Kalman Filtering with Auxiliary Dynamics. Mohamad Al Ahdab, John Leth, Zheng-Hua Tan |
| 2025 | Optimal Survey Design for Private Mean Estimation. Yu-Wei Chen, Raghu Pasupathy, Jordan Awan |
| 2025 | Optimal Task Order for Continual Learning of Multiple Tasks. Ziyan Li, Naoki Hiratani |
| 2025 | Optimal Transfer Learning for Missing Not-at-Random Matrix Completion. Akhil Jalan, Yassir Jedra, Arya Mazumdar, Soumendu Sundar Mukherjee, Purnamrita Sarkar |
| 2025 | Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization. Kaheon Kim, Rentian Yao, Changbo Zhu, Xiaohui Chen |
| 2025 | Optimal and Practical Batched Linear Bandit Algorithm. Sanghoon Yu, Min-hwan Oh |
| 2025 | Optimal transport-based conformal prediction. Gauthier Thurin, Kimia Nadjahi, Claire Boyer |
| 2025 | Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect. Ojash Neopane, Aaditya Ramdas, Aarti Singh |
| 2025 | Optimization Proxies using Limited Labeled Data and Training Time - A Semi-Supervised Bayesian Neural Network Approach. Parikshit Pareek, Abhijith Jayakumar, Kaarthik Sundar, Sidhant Misra, Deepjyoti Deka |
| 2025 | Optimization for Neural Operators can Benefit from Width. Pedro Cisneros-Velarde, Bhavesh Shrimali, Arindam Banerjee |
| 2025 | Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees. William de Vazelhes, Xiaotong Yuan, Bin Gu |
| 2025 | Optimizing Adaptive Attacks against Watermarks for Language Models. Abdulrahman Diaa, Toluwani Aremu, Nils Lukas |
| 2025 | Optimizing Language Models for Inference Time Objectives using Reinforcement Learning. Yunhao Tang, Kunhao Zheng, Gabriel Synnaeve, Rémi Munos |
| 2025 | Optimizing Large Language Model Training Using FP4 Quantization. Ruizhe Wang, Yeyun Gong, Xiao Liu, Guoshuai Zhao, Ziyue Yang, Baining Guo, Zheng-Jun Zha, Peng Cheng |
| 2025 | Optimizing Noise Distributions for Differential Privacy. Atefeh Gilani, Juan Felipe Gómez, Shahab Asoodeh, Flávio P. Calmon, Oliver Kosut, Lalitha Sankar |
| 2025 | Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach. Xu Zhang, Kaidi Xu, Ziqing Hu, Ren Wang |
| 2025 | Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design. Marino Kühne, Panagiotis D. Grontas, Giulia De Pasquale, Giuseppe Belgioioso, Florian Dörfler, John Lygeros |
| 2025 | Optimizing Temperature for Language Models with Multi-Sample Inference. Weihua Du, Yiming Yang, Sean Welleck |
| 2025 | Optimizing Test-Time Compute via Meta Reinforcement Finetuning. Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur, Lewis Tunstall, Edward Emanuel Beeching, Ruslan Salakhutdinov, Aviral Kumar |
| 2025 | Oracle-MoE: Locality-preserving Routing in the Oracle Space for Memory-constrained Large Language Model Inference. Jixian Zhou, Fang Dong, Ruijun Huang, Hengjie Cao, Mengyi Chen, Yifeng Yang, Anrui Chen, Mingzhi Dong, Yujiang Wang, Dongsheng Li, David A. Clifton, Qin Lv, Rui Zhu, Chun Zhang, Fan Yang, Tun Lu, Ning Gu, Li Shang |
| 2025 | OrcaLoca: An LLM Agent Framework for Software Issue Localization. Zhongming Yu, Hejia Zhang, Yujie Zhao, Hanxian Huang, Matrix Yao, Ke Ding, Jishen Zhao |
| 2025 | Organize the Web: Constructing Domains Enhances Pre-Training Data Curation. Alexander Wettig, Kyle Lo, Sewon Min, Hannaneh Hajishirzi, Danqi Chen, Luca Soldaini |
| 2025 | Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models. Zehan Wang, Ziang Zhang, Tianyu Pang, Chao Du, Hengshuang Zhao, Zhou Zhao |
| 2025 | Origin Identification for Text-Guided Image-to-Image Diffusion Models. Wenhao Wang, Yifan Sun, Zongxin Yang, Zhentao Tan, Zhengdong Hu, Yi Yang |
| 2025 | OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference. Seungjun Shin, Jaehoon Oh, Dokwan Oh |
| 2025 | Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection. Zhiyuan Yan, Jiangming Wang, Peng Jin, Ke-Yue Zhang, Chengchun Liu, Shen Chen, Taiping Yao, Shouhong Ding, Baoyuan Wu, Li Yuan |
| 2025 | Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads. Siqi Kou, Jiachun Jin, Zhihong Liu, Chang Liu, Ye Ma, Jian Jia, Quan Chen, Peng Jiang, Zhijie Deng |
| 2025 | Oscillation-Reduced MXFP4 Training for Vision Transformers. Yuxiang Chen, Haocheng Xi, Jun Zhu, Jianfei Chen |
| 2025 | Otter: Generating Tests from Issues to Validate SWE Patches. Toufique Ahmed, Jatin Ganhotra, Rangeet Pan, Avraham Shinnar, Saurabh Sinha, Martin Hirzel |
| 2025 | Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models. Anshuman Chhabra, Bo Li, Jian Chen, Prasant Mohapatra, Hongfu Liu |
| 2025 | Outlier-Aware Post-Training Quantization for Discrete Graph Diffusion Models. Zheng Gong, Ying Sun |
| 2025 | Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models. Siddarth Venkatraman, Mohsin Hasan, Minsu Kim, Luca Scimeca, Marcin Sendera, Yoshua Bengio, Glen Berseth, Nikolay Malkin |
| 2025 | Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling. Hongzhi Huang, Defa Zhu, Banggu Wu, Yutao Zeng, Ya Wang, Qiyang Min, Xun Zhou |
| 2025 | Overcoming Multi-step Complexity in Multimodal Theory-of-Mind Reasoning: A Scalable Bayesian Planner. Chunhui Zhang, Zhongyu Ouyang, Kwonjoon Lee, Nakul Agarwal, Sean Dae Houlihan, Soroush Vosoughi, Shao-Yuan Lo |
| 2025 | Overcoming Non-monotonicity in Transducer-based Streaming Generation. Zhengrui Ma, Yang Feng, Min Zhang |
| 2025 | Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan. Jaemoo Choi, Jaewoong Choi, Dohyun Kwon |
| 2025 | Overcoming Vocabulary Mismatch: Vocabulary-agnostic Teacher Guided Language Modeling. Haebin Shin, Lei Ji, Xiao Liu, Yeyun Gong |
| 2025 | Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization. Chenbei Lu, Laixi Shi, Zaiwei Chen, Chenye Wu, Adam Wierman |
| 2025 | Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination's Impact on Machine Translation. Muhammed Yusuf Kocyigit, Eleftheria Briakou, Daniel Deutsch, Jiaming Luo, Colin Cherry, Markus Freitag |
| 2025 | Overtrained Language Models Are Harder to Fine-Tune. Jacob Mitchell Springer, Sachin Goyal, Kaiyue Wen, Tanishq Kumar, Xiang Yue, Sadhika Malladi, Graham Neubig, Aditi Raghunathan |
| 2025 | P(all-atom) Is Unlocking New Path For Protein Design. Wei Qu, Jiawei Guan, Rui Ma, Ke Zhai, Weikun Wu, Haobo Wang |
| 2025 | PAC Learning with Improvements. Idan Attias, Avrim Blum, Keziah Naggita, Donya Saless, Dravyansh Sharma, Matthew R. Walter |
| 2025 | PAC-Bayes Analysis for Recalibration in Classification. Masahiro Fujisawa, Futoshi Futami |
| 2025 | PAK-UCB Contextual Bandit: An Online Learning Approach to Prompt-Aware Selection of Generative Models and LLMs. Xiaoyan Hu, Ho-fung Leung, Farzan Farnia |
| 2025 | PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling. Avery Ma, Yangchen Pan, Amir-massoud Farahmand |
| 2025 | PARM: Multi-Objective Test-Time Alignment via Preference-Aware Autoregressive Reward Model. Baijiong Lin, Weisen Jiang, Yuancheng Xu, Hao Chen, Ying-Cong Chen |
| 2025 | PARQ: Piecewise-Affine Regularized Quantization. Lisa Jin, Jianhao Ma, Zechun Liu, Andrey Gromov, Aaron Defazio, Lin Xiao |
| 2025 | PASS: Private Attributes Protection with Stochastic Data Substitution. Yizhuo Chen, Chun-Fu Chen, Hsiang Hsu, Shaohan Hu, Tarek F. Abdelzaher |
| 2025 | PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation via Few-Shot Private Data and Generative APIs. Jianqing Zhang, Yang Liu, Jie Fu, Yang Hua, Tianyuan Zou, Jian Cao, Qiang Yang |
| 2025 | PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs. Mauricio Soroco, Jialin Song, Mengzhou Xia, Kye Emond, Weiran Sun, Wuyang Chen |
| 2025 | PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations. Benjamin J. Holzschuh, Qiang Liu, Georg Kohl, Nils Thuerey |
| 2025 | PDUDT: Provable Decentralized Unlearning under Dynamic Topologies. Jing Qiao, Yu Liu, Zengzhe Chen, Mingyi Li, Yuan Yuan, Xiao Zhang, Dongxiao Yu |
| 2025 | PEAKS: Selecting Key Training Examples Incrementally via Prediction Error Anchored by Kernel Similarity. Mustafa Burak Gurbuz, Xingyu Zheng, Constantine Dovrolis |
| 2025 | PEINR: A Physics-enhanced Implicit Neural Representation for High-Fidelity Flow Field Reconstruction. Liming Shen, Liang Deng, Chongke Bi, Yu Wang, Xinhai Chen, Yueqing Wang, Jie Liu |
| 2025 | PENCIL: Long Thoughts with Short Memory. Chenxiao Yang, Nathan Srebro, David McAllester, Zhiyuan Li |
| 2025 | PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting for Novel View Synthesis. Sunghwan Hong, Jaewoo Jung, Heeseong Shin, Jisang Han, Jiaolong Yang, Chong Luo, Seungryong Kim |
| 2025 | PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning. Dongchi Huang, Jiaqi Wang, Yang Li, Chunhe Xia, Tianle Zhang, Kaige Zhang |
| 2025 | PILAF: Optimal Human Preference Sampling for Reward Modeling. Yunzhen Feng, Ariel Kwiatkowski, Kunhao Zheng, Julia Kempe, Yaqi Duan |
| 2025 | PINNsAgent: Automated PDE Surrogation with Large Language Models. Qingpo Wuwu, Chonghan Gao, Tianyu Chen, Yihang Huang, Yuekai Zhang, Jianing Wang, Jianxin Li, Haoyi Zhou, Shanghang Zhang |
| 2025 | PIPA: Preference Alignment as Prior-Informed Statistical Estimation. Junbo Li, Zhangyang Wang, Qiang Liu |
| 2025 | PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Drop. Chenyu Li, Oscar Michel, Xichen Pan, Sainan Liu, Mike Roberts, Saining Xie |
| 2025 | POQD: Performance-Oriented Query Decomposer for Multi-vector retrieval. Yaoyang Liu, Junlin Li, Yinjun Wu, Zhen Chen |
| 2025 | POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference Optimization. Batuhan K. Karaman, Ishmam Zabir, Alon Benhaim, Vishrav Chaudhary, Mert R. Sabuncu, Xia Song |
| 2025 | PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design. Zhenqiao Song, Tianxiao Li, Lei Li, Martin Renqiang Min |
| 2025 | PRIME: Deep Imbalanced Regression with Proxies. Jongin Lim, Sucheol Lee, Daeho Um, Sung-Un Park, Jinwoo Shin |
| 2025 | PROTOCOL: Partial Optimal Transport-enhanced Contrastive Learning for Imbalanced Multi-view Clustering. Xuqian Xue, Yiming Lei, Qi Cai, Hongming Shan, Junping Zhang |
| 2025 | PTTA: Purifying Malicious Samples for Test-Time Model Adaptation. Jing Ma, Hanlin Li, Xiang Xiang |
| 2025 | Pairwise Maximum Likelihood For Multi-Class Logistic Regression Model With Multiple Rare Classes. Xuetong Li, Danyang Huang, Hansheng Wang |
| 2025 | PaperBench: Evaluating AI's Ability to Replicate AI Research. Giulio Starace, Oliver Jaffe, Dane Sherburn, James Aung, Jun Shern Chan, Leon Maksin, Rachel Dias, Evan Mays, Benjamin Kinsella, Wyatt Thompson, Johannes Heidecke, Amelia Glaese, Tejal Patwardhan |
| 2025 | Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models. Huanjian Zhou, Masashi Sugiyama |
| 2025 | ParallelComp: Parallel Long-Context Compressor for Length Extrapolation. Jing Xiong, Jianghan Shen, Chuanyang Zheng, Zhongwei Wan, Chenyang Zhao, Chiwun Yang, Fanghua Ye, Hongxia Yang, Lingpeng Kong, Ngai Wong |
| 2025 | Parameter-Efficient Fine-Tuning of State Space Models. Kevin Galim, Wonjun Kang, Yuchen Zeng, Hyung Il Koo, Kangwook Lee |
| 2025 | Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models. Samira Abnar, Harshay Shah, Dan Busbridge, Alaaeldin El-Nouby, Joshua M. Susskind, Vimal Thilak |
| 2025 | Parametric Scaling Law of Tuning Bias in Conformal Prediction. Hao Zeng, Kangdao Liu, Bingyi Jing, Hongxin Wei |
| 2025 | Pareto Merging: Multi-Objective Optimization for Preference-Aware Model Merging. Weiyu Chen, James T. Kwok |
| 2025 | Pareto-Optimal Fronts for Benchmarking Symbolic Regression Algorithms. Kei Sen Fong, Mehul Motani |
| 2025 | Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Search. Ziyad Benomar, Lorenzo Croissant, Vianney Perchet, Spyros Angelopoulos |
| 2025 | Pareto-frontier Entropy Search with Variational Lower Bound Maximization. Masanori Ishikura, Masayuki Karasuyama |
| 2025 | Parrot: Multilingual Visual Instruction Tuning. Hai-Long Sun, Da-Wei Zhou, Yang Li, Shiyin Lu, Chao Yi, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, De-Chuan Zhan, Han-Jia Ye |
| 2025 | Partially Observable Reinforcement Learning with Memory Traces. Onno Eberhard, Michael Muehlebach, Claire Vernade |
| 2025 | Partition First, Embed Later: Laplacian-Based Feature Partitioning for Refined Embedding and Visualization of High-Dimensional Data. Erez Peterfreund, Ofir Lindenbaum, Yuval Kluger, Boris Landa |
| 2025 | Patch-wise Structural Loss for Time Series Forecasting. Dilfira Kudrat, Zongxia Xie, Yanru Sun, Tianyu Jia, Qinghua Hu |
| 2025 | PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification. Hongwei Li, Yuheng Tang, Shiqi Wang, Wenbo Guo |
| 2025 | Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data. Jeonghye Kim, Yongjae Shin, Whiyoung Jung, Sunghoon Hong, Deunsol Yoon, Youngchul Sung, Kanghoon Lee, Woohyung Lim |
| 2025 | PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion. Sophia Tang, Yinuo Zhang, Pranam Chatterjee |
| 2025 | Perception in Reflection. Yana Wei, Liang Zhao, Kangheng Lin, En Yu, Yuang Peng, Runpei Dong, Jianjian Sun, Haoran Wei, Zheng Ge, Xiangyu Zhang, Vishal M. Patel |
| 2025 | Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting. Hongbi Zhou, Zhangkai Ni |
| 2025 | Perceptually Constrained Precipitation Nowcasting Model. Wenzhi Feng, Xutao Li, Zhe Wu, Kenghong Lin, Demin Yu, Yunming Ye, Yaowei Wang |
| 2025 | Peri-LN: Revisiting Normalization Layer in the Transformer Architecture. Jeonghoon Kim, Byeongchan Lee, Cheonbok Park, Yeontaek Oh, Beomjun Kim, Taehwan Yoo, Seongjin Shin, Dongyoon Han, Jinwoo Shin, Kang Min Yoo |
| 2025 | Peripheral Memory for LLMs: Integration of Sequential Memory Banks with Adaptive Querying. Songlin Zhai, Yuan Meng, Yongrui Chen, Yiwei Wang, Guilin Qi |
| 2025 | Permutation Equivariant Neural Networks for Symmetric Tensors. Edward Pearce-Crump |
| 2025 | Permutation-Free High-Order Interaction Tests. Zhaolu Liu, Robert L. Peach, Mauricio Barahona |
| 2025 | Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data. Xinshuai Dong, Ignavier Ng, Boyang Sun, Haoyue Dai, Guang-Yuan Hao, Shunxing Fan, Peter Spirtes, Yumou Qiu, Kun Zhang |
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| 2025 | Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning. Chi Zhang, Ziying Jia, George K. Atia, Sihong He, Yue Wang |
| 2025 | Pfeife: Automatic Pipeline Parallelism for PyTorch. Ho Young Jhoo, Chung-Kil Hur, Nuno P. Lopes |
| 2025 | PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation. Albert Gong, Kamile Stankeviciute, Chao Wan, Anmol Kabra, Raphael Thesmar, Johann Lee, Julius Klenke, Carla P. Gomes, Kilian Q. Weinberger |
| 2025 | Phase and Amplitude-aware Prompting for Enhancing Adversarial Robustness. Yibo Xu, Dawei Zhou, Decheng Liu, Nannan Wang |
| 2025 | Phase transitions for the existence of unregularized M-estimators in single index models. Takuya Koriyama, Pierre C. Bellec |
| 2025 | Physics Aware Neural Networks for Unsupervised Binding Energy Prediction. Ke Liu, Hao Cheng, Chunhua Shen |
| 2025 | Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification. Jan Blechschmidt, Tom-Christian Riemer, Max Winkler, Martin Stoll, Jan-Frederik Pietschmann |
| 2025 | Physics-Informed Generative Modeling of Wireless Channels. Benedikt Böck, Andreas Oeldemann, Timo Mayer, Francesco Rossetto, Wolfgang Utschick |
| 2025 | Physics-Informed Weakly Supervised Learning For Interatomic Potentials. Makoto Takamoto, Viktor Zaverkin, Mathias Niepert |
| 2025 | Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models. Congcong Zhu, Xiaoyan Xu, Jiayue Han, Jingrun Chen |
| 2025 | PiD: Generalized AI-Generated Images Detection with Pixelwise Decomposition Residuals. Xinghe Fu, Zhiyuan Yan, Zheng Yang, Taiping Yao, Yandan Zhao, Shouhong Ding, Xi Li |
| 2025 | PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities. Daniel Zilberg, Ron Levie |
| 2025 | Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule. Keyue Qiu, Yuxuan Song, Zhehuan Fan, Peidong Liu, Zhe Zhang, Mingyue Zheng, Hao Zhou, Wei-Ying Ma |
| 2025 | PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization. Xinyi Wan, Penghui Qi, Guangxing Huang, Min Lin, Jialin Li |
| 2025 | Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models. Jialin Zhao, Yingtao Zhang, Carlo Vittorio Cannistraci |
| 2025 | Pixel-level Certified Explanations via Randomized Smoothing. Alaa Anani, Tobias Lorenz, Mario Fritz, Bernt Schiele |
| 2025 | Pixel2Feature Attack (P2FA): Rethinking the Perturbed Space to Enhance Adversarial Transferability. Renpu Liu, Hao Wu, Jiawei Zhang, Xin Cheng, Xiangyang Luo, Bin Ma, Jinwei Wang |
| 2025 | Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks. Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon, Hiroki Furuta, Gopala Anumanchipalli, Kurt Keutzer, Amir Gholami |
| 2025 | Plausible Token Amplification for Improving Accuracy of Differentially Private In-Context Learning Based on Implicit Bayesian Inference. Yusuke Yamasaki, Kenta Niwa, Daiki Chijiwa, Takumi Fukami, Takayuki Miura |
| 2025 | PlaySlot: Learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning. Angel Villar-Corrales, Sven Behnke |
| 2025 | Playmate: Flexible Control of Portrait Animation via 3D-Implicit Space Guided Diffusion. Xingpei Ma, Jiaran Cai, Yuansheng Guan, Shenneng Huang, Qiang Zhang, Shunsi Zhang |
| 2025 | Point Cloud Dataset Distillation. Deyu Bo, Xinchao Wang |
| 2025 | Point-Level Topological Representation Learning on Point Clouds. Vincent Peter Grande, Michael T. Schaub |
| 2025 | Pointwise Information Measures as Confidence Estimators in Deep Neural Networks: A Comparative Study. Shelvia Wongso, Rohan Ghosh, Mehul Motani |
| 2025 | PoisonBench: Assessing Language Model Vulnerability to Poisoned Preference Data. Tingchen Fu, Mrinank Sharma, Philip Torr, Shay B. Cohen, David Krueger, Fazl Barez |
| 2025 | PoisonedEye: Knowledge Poisoning Attack on Retrieval-Augmented Generation based Large Vision-Language Models. Chenyang Zhang, Xiaoyu Zhang, Jian Lou, Kai Wu, Zilong Wang, Xiaofeng Chen |
| 2025 | PokéChamp: an Expert-level Minimax Language Agent. Seth Karten, Andy Luu Nguyen, Chi Jin |
| 2025 | Policy Design for Two-sided Platforms with Participation Dynamics. Haruka Kiyohara, Fan Yao, Sarah Dean |
| 2025 | Policy Filtration for RLHF to Mitigate Noise in Reward Models. Chuheng Zhang, Wei Shen, Li Zhao, Xuyun Zhang, Xiaolong Xu, Wanchun Dou, Jiang Bian |
| 2025 | Policy Gradient with Tree Expansion. Gal Dalal, Assaf Hallak, Gugan Thoppe, Shie Mannor, Gal Chechik |
| 2025 | Policy Guided Tree Search for Enhanced LLM Reasoning. Yang Li |
| 2025 | Policy Optimization for CMDPs with Bandit Feedback: Learning Stochastic and Adversarial Constraints. Francesco Emanuele Stradi, Anna Lunghi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti |
| 2025 | Policy Regularization on Globally Accessible States in Cross-Dynamics Reinforcement Learning. Zhenghai Xue, Lang Feng, Jiacheng Xu, Kang Kang, Xiang Wen, Bo An, Shuicheng Yan |
| 2025 | Policy-Regret Minimization in Markov Games with Function Approximation. Thanh Nguyen-Tang, Raman Arora |
| 2025 | Policy-labeled Preference Learning: Is Preference Enough for RLHF? Taehyun Cho, Seokhun Ju, Seungyub Han, Dohyeong Kim, Kyungjae Lee, Jungwoo Lee |
| 2025 | Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications. Maria Despoina Siampou, Jialiang Li, John Krumm, Cyrus Shahabi, Hua Lu |
| 2025 | PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Models. Fanmeng Wang, Wentao Guo, Qi Ou, Hongshuai Wang, Haitao Lin, Hongteng Xu, Zhifeng Gao |
| 2025 | Polynomial Time Learning Augmented Algorithms for NP-hard Permutation Problems. Evripidis Bampis, Bruno Escoffier, Dimitris Fotakis, Panagiotis Patsilinakos, Michalis Xefteris |
| 2025 | Polynomial-Delay MAG Listing with Novel Locally Complete Orientation Rules. Tian-Zuo Wang, Wen-Bo Du, Zhi-Hua Zhou |
| 2025 | Polynomial-Time Approximability of Constrained Reinforcement Learning. Jeremy McMahan |
| 2025 | Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models. Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida, Kuniko Saito, Susumu Takeuchi |
| 2025 | Positional Attention: Expressivity and Learnability of Algorithmic Computation. Artur Back de Luca, George Giapitzakis, Shenghao Yang, Petar Velickovic, Kimon Fountoulakis |
| 2025 | Positional Encoding meets Persistent Homology on Graphs. Yogesh Verma, Amauri H. Souza, Vikas K. Garg |
| 2025 | Positive-unlabeled AUC Maximization under Covariate Shift. Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Kazuki Adachi, Yasuhiro Fujiwara |
| 2025 | Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization. Taeyoung Yun, Kiyoung Om, Jaewoo Lee, Sujin Yun, Jinkyoo Park |
| 2025 | Potemkin Understanding in Large Language Models. Marina Mancoridis, Bec Weeks, Keyon Vafa, Sendhil Mullainathan |
| 2025 | Power Mean Estimation in Stochastic Continuous Monte-Carlo Tree Search. Tuan Dam |
| 2025 | Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG. Xinxu Wei, Kanhao Zhao, Yong Jiao, Hua Xie, Lifang He, Yu Zhang |
| 2025 | Pre-training Auto-regressive Robotic Models with 4D Representations. Dantong Niu, Yuvan Sharma, Haoru Xue, Giscard Biamby, Junyi Zhang, Ziteng Ji, Trevor Darrell, Roei Herzig |
| 2025 | Preconditioned Riemannian Gradient Descent Algorithm for Low-Multilinear-Rank Tensor Completion. Yuanwei Zhang, Fengmiao Bian, Xiaoqun Zhang, Jian-Feng Cai |
| 2025 | Predicting High-precision Depth on Low-Precision Devices Using 2D Hilbert Curves. Mykhailo Uss, Ruslan Yermolenko, Oleksii Shashko, Olena Kolodiazhna, Ivan Safonov, Volodymyr Savin, Yoon-Jae Yeo, Seo-Won Ji, Jaeyun Jeong |
| 2025 | Predicting mutational effects on protein binding from folding energy. Arthur Deng, Karsten D. Householder, Fang Wu, K. Christopher Garcia, Brian L. Trippe |
| 2025 | Predicting the Susceptibility of Examples to Catastrophic Forgetting. Guy Hacohen, Tinne Tuytelaars |
| 2025 | Prediction models that learn to avoid missing values. Lena Stempfle, Anton Matsson, Newton Mwai Kinyanjui, Fredrik D. Johansson |
| 2025 | Prediction via Shapley Value Regression. Amr Alkhatib, Roman Bresson, Henrik Boström, Michalis Vazirgiannis |
| 2025 | Prediction-Aware Learning in Multi-Agent Systems. Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Oliviero Durmus |
| 2025 | Prediction-Powered Adaptive Shrinkage Estimation. Sida Li, Nikolaos Ignatiadis |
| 2025 | Prediction-Powered E-Values. Daniel Csillag, Cláudio José Struchiner, Guilherme Tegoni Goedert |
| 2025 | Predictive Data Selection: The Data That Predicts Is the Data That Teaches. Kashun Shum, Yuzhen Huang, Hongjian Zou, Qi Ding, Yixuan Liao, Xiaoxin Chen, Qian Liu, Junxian He |
| 2025 | Predictive Performance of Deep Quantum Data Re-uploading Models. Xin Wang, Hanxiao Tao, Rebing Wu |
| 2025 | Preference Adaptive and Sequential Text-to-Image Generation. Ofir Nabati, Guy Tennenholtz, Chih-Wei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, Craig Boutilier |
| 2025 | Preference Controllable Reinforcement Learning with Advanced Multi-Objective Optimization. Yucheng Yang, Tianyi Zhou, Mykola Pechenizkiy, Meng Fang |
| 2025 | Preference Learning for AI Alignment: a Causal Perspective. Kasia Kobalczyk, Mihaela van der Schaar |
| 2025 | Preference Optimization for Combinatorial Optimization Problems. Mingjun Pan, Guanquan Lin, You-Wei Luo, Bin Zhu, Zhien Dai, Lijun Sun, Chun Yuan |
| 2025 | Preference learning made easy: Everything should be understood through win rate. Lily H. Zhang, Rajesh Ranganath |
| 2025 | Preference-CFR: Beyond Nash Equilibrium for Better Game Strategies. Qi Ju, Thomas Tellier, Meng Sun, Zhemei Fang, Yunfeng Luo |
| 2025 | Premise-Augmented Reasoning Chains Improve Error Identification in Math reasoning with LLMs. Sagnik Mukherjee, Abhinav Chinta, Takyoung Kim, Tarun Anoop Sharma, Dilek Hakkani-Tur |
| 2025 | Preserving AUC Fairness in Learning with Noisy Protected Groups. Mingyang Wu, Li Lin, Wenbin Zhang, Xin Wang, Zhenhuan Yang, Shu Hu |
| 2025 | Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation. Mohit Pandey, Gopeshh Subbaraj, Artem Cherkasov, Martin Ester, Emmanuel Bengio |
| 2025 | Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All. Ermis Soumalias, Jakob Heiss, Jakob Weissteiner, Sven Seuken |
| 2025 | Primal-Dual Neural Algorithmic Reasoning. Yu He, Ellen Vitercik |
| 2025 | Primitive Vision: Improving Diagram Understanding in MLLMs. Shan Zhang, Aotian Chen, Yanpeng Sun, Jindong Gu, Yi-Yu Zheng, Piotr Koniusz, Kai Zou, Anton van den Hengel, Yuan Xue |
| 2025 | Primphormer: Efficient Graph Transformers with Primal Representations. Mingzhen He, Ruikai Yang, Hanling Tian, Youmei Qiu, Xiaolin Huang |
| 2025 | Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents. Junyan Liu, Lillian J. Ratliff |
| 2025 | Principled Algorithms for Optimizing Generalized Metrics in Binary Classification. Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2025 | Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples. Chengqian Gao, Haonan Li, Liu Liu, Zeke Xie, Peilin Zhao, Zhiqiang Xu |
| 2025 | Prior Knowledge Guided Neural Architecture Generation. Jingrong Xie, Han Ji, Yanan Sun |
| 2025 | Privacy Amplification Through Synthetic Data: Insights from Linear Regression. Clément Pierquin, Aurélien Bellet, Marc Tommasi, Matthieu Boussard |
| 2025 | Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting. Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann |
| 2025 | Privacy Attacks on Image AutoRegressive Models. Antoni Kowalczuk, Jan Dubinski, Franziska Boenisch, Adam Dziedzic |
| 2025 | Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. Roie Reshef, Kfir Yehuda Levy |
| 2025 | Privacy-Shielded Image Compression: Defending Against Exploitation from Vision-Language Pretrained Models. Xuelin Shen, Jiayin Xu, Kangsheng Yin, Wenhan Yang |
| 2025 | Private Federated Learning using Preference-Optimized Synthetic Data. Charlie Hou, Mei-Yu Wang, Yige Zhu, Daniel Lazar, Giulia Fanti |
| 2025 | Private Lossless Multiple Release. Joel Daniel Andersson, Lukas Retschmeier, Boel Nelson, Rasmus Pagh |
| 2025 | Private Model Personalization Revisited. Conor Snedeker, Xinyu Zhou, Raef Bassily |
| 2025 | ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation. Tianci Bu, Le Zhou, Wenchuan Yang, Jianhong Mou, Kang Yang, Suoyi Tan, Feng Yao, Jingyuan Wang, Xin Lu |
| 2025 | ProSec: Fortifying Code LLMs with Proactive Security Alignment. Xiangzhe Xu, Zian Su, Jinyao Guo, Kaiyuan Zhang, Zhenting Wang, Xiangyu Zhang |
| 2025 | Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty. Meera Hahn, Wenjun Zeng, Nithish Kannen, Rich Galt, Kartikeya Badola, Been Kim, Zi Wang |
| 2025 | Probabilistic Factorial Experimental Design for Combinatorial Interventions. Divya Shyamal, Jiaqi Zhang, Caroline Uhler |
| 2025 | Probabilistic Group Mask Guided Discrete Optimization for Incremental Learning. Fengqiang Wan, Yang Yang |
| 2025 | Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes. Jie Liu, Pan Zhou, Zehao Xiao, Jiayi Shen, Wenzhe Yin, Jan-Jakob Sonke, Efstratios Gavves |
| 2025 | Probably Approximately Global Robustness Certification. Peter Blohm, Patrick Indri, Thomas Gärtner, Sagar Malhotra |
| 2025 | Probing Visual Language Priors in VLMs. Tiange Luo, Ang Cao, Gunhee Lee, Justin Johnson, Honglak Lee |
| 2025 | Procurement Auctions via Approximately Optimal Submodular Optimization. Yuan Deng, Amin Karbasi, Vahab Mirrokni, Renato Paes Leme, Grigoris Velegkas, Song Zuo |
| 2025 | Product of Experts with LLMs: Boosting Performance on ARC Is a Matter of Perspective. Daniel Franzen, Jan Disselhoff, David Hartmann |
| 2025 | Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale. Fan Zhou, Zengzhi Wang, Qian Liu, Junlong Li, Pengfei Liu |
| 2025 | Progressive Tempering Sampler with Diffusion. Severi Rissanen, Ruikang Ouyang, Jiajun He, Wenlin Chen, Markus Heinonen, Arno Solin, José Miguel Hernández-Lobato |
| 2025 | Progressively Label Enhancement for Large Language Model Alignment. Biao Liu, Ning Xu, Xin Geng |
| 2025 | Projection Optimization: A General Framework for Multi-Objective and Multi-Group RLHF. Nuoya Xiong, Aarti Singh |
| 2025 | Projection Pursuit Density Ratio Estimation. Meilin Wang, Wei Huang, Mingming Gong, Zheng Zhang |
| 2025 | Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models. Ngoc-Quan Pham, Tuan Truong, Quyen Tran, Tan Minh Nguyen, Dinh Phung, Trung Le |
| 2025 | Prompt-based Depth Pruning of Large Language Models. Juyun Wee, Minjae Park, Jaeho Lee |
| 2025 | Prompt-to-Leaderboard: Prompt-Adaptive LLM Evaluations. Evan Frick, Connor Chen, Joseph Tennyson, Tianle Li, Wei-Lin Chiang, Anastasios Nikolas Angelopoulos, Ion Stoica |
| 2025 | ProofAug: Efficient Neural Theorem Proving via Fine-grained Proof Structure Analysis. Haoxiong Liu, Jiacheng Sun, Zhenguo Li, Andrew C. Yao |
| 2025 | Propagate and Inject: Revisiting Propagation-Based Feature Imputation for Graphs with Partially Observed Features. Daeho Um, Sunoh Kim, Jiwoong Park, Jongin Lim, Seong-Jin Ahn, Seulki Park |
| 2025 | Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble. Atsushi Nitanda, Anzelle Lee, Damian Tan Xing Kai, Mizuki Sakaguchi, Taiji Suzuki |
| 2025 | Proposer-Agent-Evaluator (PAE): Autonomous Skill Discovery For Foundation Model Internet Agents. Yifei Zhou, Qianlan Yang, Kaixiang Lin, Min Bai, Xiong Zhou, Yu-Xiong Wang, Sergey Levine, Li Erran Li |
| 2025 | Protein Structure Tokenization: Benchmarking and New Recipe. Xinyu Yuan, Zichen Wang, Marcus D. Collins, Huzefa Rangwala |
| 2025 | Proto Successor Measure: Representing the Behavior Space of an RL Agent. Siddhant Agarwal, Harshit Sikchi, Peter Stone, Amy Zhang |
| 2025 | Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction. Ruben Weitzman, Peter Mørch Groth, Lood van Niekerk, Aoi Otani, Yarin Gal, Debora Susan Marks, Pascal Notin |
| 2025 | Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent. Donghwa Kim, Jaewook Lee, Chulhee Yun |
| 2025 | Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models. Taj Jones-McCormick, Aukosh Jagannath, Subhabrata Sen |
| 2025 | Provable Efficiency of Guidance in Diffusion Models for General Data Distribution. Gen Li, Yuchen Jiao |
| 2025 | Provable In-Context Vector Arithmetic via Retrieving Task Concepts. Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Qingfu Zhang, Hau-San Wong, Taiji Suzuki |
| 2025 | Provable Length Generalization in Sequence Prediction via Spectral Filtering. Annie Marsden, Evan Dogariu, Naman Agarwal, Xinyi Chen, Daniel Suo, Elad Hazan |
| 2025 | Provable Maximum Entropy Manifold Exploration via Diffusion Models. Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause |
| 2025 | Provable Policy Gradient for Robust Average-Reward MDPs Beyond Rectangularity. Qiuhao Wang, Yuqi Zha, Chin Pang Ho, Marek Petrik |
| 2025 | Provable Zero-Shot Generalization in Offline Reinforcement Learning. Zhiyong Wang, Chen Yang, John C. S. Lui, Dongruo Zhou |
| 2025 | Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent. Ya-Chi Chu, Wenzhi Gao, Yinyu Ye, Madeleine Udell |
| 2025 | Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing. Yuan Xin, Dingfan Chen, Michael Backes, Xiao Zhang |
| 2025 | Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition. Zichen Wang, Chuanhao Li, Huazheng Wang |
| 2025 | Provably Efficient Exploration in Inverse Constrained Reinforcement Learning. Bo Yue, Jian Li, Guiliang Liu |
| 2025 | Provably Efficient RL for Linear MDPs under Instantaneous Safety Constraints in Non-Convex Feature Spaces. Amirhossein Roknilamouki, Arnob Ghosh, Ming Shi, Fatemeh Nourzad, Eylem Ekici, Ness B. Shroff |
| 2025 | Provably Improving Generalization of Few-shot models with Synthetic Data. Lan-Cuong Nguyen, Quan Nguyen-Tri, Bang Tran Khanh, Dung D. Le, Long Tran-Thanh, Khoat Than |
| 2025 | Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead. Won-Jun Jang, Hyeon-Seo Park, Si-Hyeon Lee |
| 2025 | Proxsparse: Regularized Learning of Semi-Structured Sparsity masks for Pretrained LLMS. Hongyi Liu, Rajarshi Saha, Zhen Jia, Youngsuk Park, Jiaji Huang, Shoham Sabach, Yu-Xiang Wang, George Karypis |
| 2025 | Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting. Chen Huang, Skyler Seto, Hadi Pouransari, Mehrdad Farajtabar, Raviteja Vemulapalli, Fartash Faghri, Oncel Tuzel, Barry-John Theobald, Joshua M. Susskind |
| 2025 | Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction. Harit Vishwakarma, Alan Mishler, Thomas Cook, Niccolò Dalmasso, Natraj Raman, Sumitra Ganesh |
| 2025 | Pruning for GNNs: Lower Complexity with Comparable Expressiveness. Dun Ma, Jianguo Chen, Wenguo Yang, Suixiang Gao, Shengminjie Chen |
| 2025 | Putnam-AXIOM: A Functional & Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs. Aryan Gulati, Brando Miranda, Eric Chen, Emily Xia, Kai Fronsdal, Bruno de Moraes Dumont, Sanmi Koyejo |
| 2025 | Puzzle: Distillation-Based NAS for Inference-Optimized LLMs. Akhiad Bercovich, Tomer Ronen, Talor Abramovich, Nir Ailon, Nave Assaf, Mohammad Dabbah, Ido Galil, Amnon Geifman, Yonatan Geifman, Izhak Golan, Netanel Haber, Ehud Karpas, Roi Koren, Itay Levy, Pavlo Molchanov, Shahar Mor, Zach Moshe, Najeeb Nabwani, Omri Puny, Ran Rubin, Itamar Schen, Ido Shahaf, Oren Tropp, Omer Ullman Argov, Ran Zilberstein, Ran El-Yaniv |
| 2025 | PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models. Alejandro Velez-Arce, Marinka Zitnik |
| 2025 | Q-Supervised Contrastive Representation: A State Decoupling Framework for Safe Offline Reinforcement Learning. Zhihe Yang, Yunjian Xu, Yang Zhang |
| 2025 | Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers. Weilun Feng, Chuanguang Yang, Haotong Qin, Xiangqi Li, Yu Wang, Zhulin An, Libo Huang, Boyu Diao, Zixiang Zhao, Yongjun Xu, Michele Magno |
| 2025 | QEM-Bench: Benchmarking Learning-based Quantum Error Mitigation and QEMFormer as a Multi-ranged Context Learning Baseline. Tianyi Bao, Ruizhe Zhong, Xinyu Ye, Yehui Tang, Junchi Yan |
| 2025 | QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search. Zongyu Lin, Yao Tang, Xingcheng Yao, Da Yin, Ziniu Hu, Yizhou Sun, Kai-Wei Chang |
| 2025 | QMamba: On First Exploration of Vision Mamba for Image Quality Assessment. Fengbin Guan, Xin Li, Zihao Yu, Yiting Lu, Zhibo Chen |
| 2025 | QPRL : Learning Optimal Policies with Quasi-Potential Functions for Asymmetric Traversal. Jumman Hossain, Nirmalya Roy |
| 2025 | QT-DoG: Quantization-Aware Training for Domain Generalization. Saqib Javed, Hieu Le, Mathieu Salzmann |
| 2025 | QUTE: Quantifying Uncertainty in TinyML models with Early-exit-assisted ensembles for model-monitoring. Nikhil Pratap Ghanathe, Steven J. E. Wilton |
| 2025 | QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration. Hamid Reza Imani, Jiaxin Peng, Peiman Mohseni, Abdolah Amirany, Tarek A. El-Ghazawi |
| 2025 | QuEST: Stable Training of LLMs with 1-Bit Weights and Activations. Andrei Panferov, Jiale Chen, Soroush Tabesh, Mahdi Nikdan, Dan Alistarh |
| 2025 | QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions. Zhun Deng, Thomas P. Zollo, Benjamin Eyre, Amogh Inamdar, David Madras, Richard S. Zemel |
| 2025 | QuRe: Query-Relevant Retrieval through Hard Negative Sampling in Composed Image Retrieval. Jaehyun Kwak, Ramahdani Muhammad Izaaz Inhar, Se-Young Yun, Sung-Ju Lee |
| 2025 | Quadratic Upper Bound for Boosting Robustness. Euijin You, Hyang-Won Lee |
| 2025 | Quadruple Attention in Many-body Systems for Accurate Molecular Property Predictions. Jiahua Rao, Dahao Xu, Wentao Wei, Yicong Chen, Mingjun Yang, Yuedong Yang |
| 2025 | Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models. Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin, Kai-Chiang Wu, Mohamed S. Abdelfattah, Diana Marculescu |
| 2025 | QuanONet: Quantum Neural Operator with Application to Differential Equation. Ruocheng Wang, Zhuo Xia, Ge Yan, Junchi Yan |
| 2025 | QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache. Rishabh Tiwari, Haocheng Xi, Aditya Tomar, Coleman Richard Charles Hooper, Sehoon Kim, Maxwell Horton, Mahyar Najibi, Michael W. Mahoney, Kurt Keutzer, Amir Gholami |
| 2025 | Quantifying Memory Utilization with Effective State-Size. Rom N. Parnichkun, Neehal Tumma, Armin W. Thomas, Alessandro Moro, Qi An, Taiji Suzuki, Atsushi Yamashita, Michael Poli, Stefano Massaroli |
| 2025 | Quantifying Prediction Consistency Under Fine-tuning Multiplicity in Tabular LLMs. Faisal Hamman, Pasan Dissanayake, Saumitra Mishra, Freddy Lécué, Sanghamitra Dutta |
| 2025 | Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies. Ahmed Boughdiri, Julie Josse, Erwan Scornet |
| 2025 | Quantum Algorithms for Finite-horizon Markov Decision Processes. Bin Luo, Yuwen Huang, Jonathan Allcock, Xiaojun Lin, Shengyu Zhang, John C. S. Lui |
| 2025 | Quantum Optimization via Gradient-Based Hamiltonian Descent. Jiaqi Leng, Bin Shi |
| 2025 | Quantum Speedup for Hypergraph Sparsification. Chenghua Liu, Minbo Gao, Zhengfeng Ji, Mingsheng Ying |
| 2025 | Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes. Bhargav Ganguly, Yang Xu, Vaneet Aggarwal |
| 2025 | R*: Efficient Reward Design via Reward Structure Evolution and Parameter Alignment Optimization with Large Language Models. Pengyi Li, Jianye Hao, Hongyao Tang, Yifu Yuan, Jinbin Qiao, Zibin Dong, Yan Zheng |
| 2025 | R.I.P.: Better Models by Survival of the Fittest Prompts. Ping Yu, Weizhe Yuan, Olga Golovneva, Tianhao Wu, Sainbayar Sukhbaatar, Jason E. Weston, Jing Xu |
| 2025 | R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts. Zhongyang Li, Ziyue Li, Tianyi Zhou |
| 2025 | R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning. Harsh Goel, Mohammad Omama, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi-Pari, Sandeep P. Chinchali |
| 2025 | RAGGED: Towards Informed Design of Scalable and Stable RAG Systems. Jennifer Hsia, Afreen Shaikh, Zora Zhiruo Wang, Graham Neubig |
| 2025 | RAPID: Long-Context Inference with Retrieval-Augmented Speculative Decoding. Guanzheng Chen, Qilong Feng, Jinjie Ni, Xin Li, Michael Qizhe Shieh |
| 2025 | RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals. David Reber, Sean M. Richardson, Todd Nief, Cristina Garbacea, Victor Veitch |
| 2025 | RBench: Graduate-level Multi-disciplinary Benchmarks for LLM & MLLM Complex Reasoning Evaluation. Meng-Hao Guo, Jiajun Xu, Yi Zhang, Jiaxi Song, Haoyang Peng, Yi-Xuan Deng, Xinzhi Dong, Kiyohiro Nakayama, Zhengyang Geng, Chen Wang, Bolin Ni, Guo-Wei Yang, Yongming Rao, Houwen Peng, Han Hu, Gordon Wetzstein, Shimin Hu |
| 2025 | RE-Bench: Evaluating Frontier AI R&D Capabilities of Language Model Agents against Human Experts. Hjalmar Wijk, Tao Roa Lin, Joel Becker, Sami Jawhar, Neev Parikh, Thomas Broadley, Lawrence Chan, Michael Chen, Joshua Clymer, Jai Dhyani, Elena Ericheva, Katharyn Garcia, Brian Goodrich, Nikola Jurkovic, Megan Kinniment, Aron Lajko, Seraphina Nix, Lucas Jun Koba Sato, William Saunders, Maksym Taran, Ben West, Elizabeth Barnes |
| 2025 | RE-IMAGINE: Symbolic Benchmark Synthesis for Reasoning Evaluation. Xinnuo Xu, Rachel Lawrence, Kshitij Dubey, Atharva Pandey, Risa Ueno, Fabian Falck, Aditya V. Nori, Rahul Sharma, Amit Sharma, Javier González |
| 2025 | REG: Rectified Gradient Guidance for Conditional Diffusion Models. Zhengqi Gao, Kaiwen Zha, Tianyuan Zhang, Zihui Xue, Duane S. Boning |
| 2025 | REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective. Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Vincent Cohen-Addad, Johannes Gasteiger, Stephan Günnemann |
| 2025 | RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers. Min Zhao, Guande He, Yixiao Chen, Hongzhou Zhu, Chongxuan Li, Jun Zhu |
| 2025 | RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation. Jingxiang Qu, Wenhan Gao, Jiaxing Zhang, Xufeng Liu, Hua Wei, Haibin Ling, Yi Liu |
| 2025 | RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning. Jonas Gehring, Kunhao Zheng, Jade Copet, Vegard Mella, Taco Cohen, Gabriel Synnaeve |
| 2025 | RLTHF: Targeted Human Feedback for LLM Alignment. Yifei Xu, Tusher Chakraborty, Emre Kiciman, Bibek Aryal, Srinagesh Sharma, Songwu Lu, Ranveer Chandra |
| 2025 | ROME is Forged in Adversity: Robust Distilled Datasets via Information Bottleneck. Zheng Zhou, Wenquan Feng, Qiaosheng Zhang, Shuchang Lyu, Qi Zhao, Guangliang Cheng |
| 2025 | ROPO: Robust Preference Optimization for Large Language Models. Xize Liang, Chao Chen, Shuang Qiu, Jie Wang, Yue Wu, Zhihang Fu, Hanzhu Chen, Feng Wu, Jieping Ye |
| 2025 | ROS: A GNN-based Relax-Optimize-and-Sample Framework for Max-k-Cut Problems. Yeqing Qiu, Ye Xue, Akang Wang, Yiheng Wang, Qingjiang Shi, Zhi-Quan Luo |
| 2025 | RULEBREAKERS: Challenging LLMs at the Crossroads between Formal Logic and Human-like Reasoning. Jason Chan, Robert J. Gaizauskas, Zhixue Zhao |
| 2025 | RUN: Reversible Unfolding Network for Concealed Object Segmentation. Chunming He, Rihan Zhang, Fengyang Xiao, Chengyu Fang, Longxiang Tang, Yulun Zhang, Linghe Kong, Deng-Ping Fan, Kai Li, Sina Farsiu |
| 2025 | RWKVQuant: Quantizing the RWKV Family with Proxy Guided Hybrid of Scalar and Vector Quantization. Chen Xu, Yuxuan Yue, Zukang Xu, Xing Hu, Jiangyong Yu, Zhixuan Chen, Sifan Zhou, Zhihang Yuan, Dawei Yang |
| 2025 | RZ-NAS: Enhancing LLM-guided Neural Architecture Search via Reflective Zero-Cost Strategy. Zipeng Ji, Guanghui Zhu, Chunfeng Yuan, Yihua Huang |
| 2025 | Radio: Rate-Distortion Optimization for Large Language Model Compression. Sean I. Young |
| 2025 | Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing. Han Jiang, Xiaoyuan Yi, Zhihua Wei, Ziang Xiao, Shu Wang, Xing Xie |
| 2025 | Random Feature Representation Boosting. Nikita Zozoulenko, Thomas Cass, Lukas Gonon |
| 2025 | Random Policy Evaluation Uncovers Policies of Generative Flow Networks. Haoran He, Emmanuel Bengio, Qingpeng Cai, Ling Pan |
| 2025 | Random Registers for Cross-Domain Few-Shot Learning. Shuai Yi, Yixiong Zou, Yuhua Li, Ruixuan Li |
| 2025 | Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures. Jie Gao, Rajesh Jayaram, Benedikt Kolbe, Shay Sapir, Chris Schwiegelshohn, Sandeep Silwal, Erik Waingarten |
| 2025 | Rank-One Modified Value Iteration. Arman Sharifi Kolarijani, Tolga Ok, Peyman Mohajerin Esfahani, Mohamad Amin Sharifi Kolarijani |
| 2025 | Ranked Entropy Minimization for Continual Test-Time Adaptation. Jisu Han, Jaemin Na, Wonjun Hwang |
| 2025 | Ranked from Within: Ranking Large Multimodal Models Without Labels. Weijie Tu, Weijian Deng, Dylan Campbell, Yu Yao, Jiyang Zheng, Tom Gedeon, Tongliang Liu |
| 2025 | Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity. Tao Jin, Yue Wu, Quanquan Gu, Farzad Farnoud |
| 2025 | Rapid Overfitting of Multi-Pass SGD in Stochastic Convex Optimization. Shira Vansover-Hager, Tomer Koren, Roi Livni |
| 2025 | Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models. Ulzee An, Moonseong Jeong, Simon A. Lee, Aditya Gorla, Yuzhe Yang, Sriram Sankararaman |
| 2025 | Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger. Qi Yang, Chenghao Zhang, Lubin Fan, Kun Ding, Jieping Ye, Shiming Xiang |
| 2025 | ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding. Xingyu Fu, Minqian Liu, Zhengyuan Yang, John Corring, Yijuan Lu, Jianwei Yang, Dan Roth, Dinei A. F. Florêncio, Cha Zhang |
| 2025 | ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering. Lufei Liu, Tor M. Aamodt |
| 2025 | RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers. Xuwei Xu, Yang Li, Yudong Chen, Jiajun Liu, Sen Wang |
| 2025 | ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation. Angxiao Yue, Zichong Wang, Hongteng Xu |
| 2025 | ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification. Hyunseok Lee, Seunghyuk Oh, Jaehyung Kim, Jinwoo Shin, Jihoon Tack |
| 2025 | Reaction Graph: Towards Reaction-Level Modeling for Chemical Reactions with 3D Structures. Yingzhao Jian, Yue Zhang, Ying Wei, Hehe Fan, Yi Yang |
| 2025 | RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning. Yuanhuiyi Lyu, Xu Zheng, Lutao Jiang, Yibo Yan, Xin Zou, Huiyu Zhou, Linfeng Zhang, Xuming Hu |
| 2025 | Reasoning Limitations of Multimodal Large Language Models. A case study of Bongard Problems. Mikolaj Malkinski, Szymon Pawlonka, Jacek Mandziuk |
| 2025 | Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation. Zhuohao Yu, Weizheng Gu, Yidong Wang, Xingru Jiang, Zhengran Zeng, Jindong Wang, Wei Ye, Shikun Zhang |
| 2025 | Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment. Cheryl Li, Tianyuan Xu, Steven Y. Guo |
| 2025 | Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization. Suryanarayana Sankagiri, Jalal Etesami, Matthias Grossglauser |
| 2025 | Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning. Da Kuang, Guanwen Qiu, Junhyong Kim |
| 2025 | Rectifying Conformity Scores for Better Conditional Coverage. Vincent Plassier, Alexander Fishkov, Victor Dheur, Mohsen Guizani, Souhaib Ben Taieb, Maxim Panov, Eric Moulines |
| 2025 | Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport. Yuguang Yan, Zongyu Li, Haolin Yang, Zeqin Yang, Hao Zhou, Ruichu Cai, Zhifeng Hao |
| 2025 | Reducing Tool Hallucination via Reliability Alignment. Hongshen Xu, Zichen Zhu, Lei Pan, Zihan Wang, Su Zhu, Da Ma, Ruisheng Cao, Lu Chen, Kai Yu |
| 2025 | Reducing Variance of Stochastic Optimization for Approximating Nash Equilibria in Normal-Form Games. Linjian Meng, Wubing Chen, Wenbin Li, Tianpei Yang, Youzhi Zhang, Yang Gao |
| 2025 | Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs. Wenxin Tai, Ting Zhong, Goce Trajcevski, Fan Zhou |
| 2025 | ReferSplat: Referring Segmentation in 3D Gaussian Splatting. Shuting He, Guangquan Jie, Changshuo Wang, Yun Zhou, Shuming Hu, Guanbin Li, Henghui Ding |
| 2025 | Refined generalization analysis of the Deep Ritz Method and Physics-Informed Neural Networks. Xianliang Xu, Ye Li, Zhongyi Huang |
| 2025 | Refining Adaptive Zeroth-Order Optimization at Ease. Yao Shu, Qixin Zhang, Kun He, Zhongxiang Dai |
| 2025 | Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens. Jihwan Jeong, Xiaoyu Wang, Jingmin Wang, Scott Sanner, Pascal Poupart |
| 2025 | Reflection-Bench: Evaluating Epistemic Agency in Large Language Models. Lingyu Li, Yixu Wang, Haiquan Zhao, Shuqi Kong, Yan Teng, Chunbo Li, Yingchun Wang |
| 2025 | Reflection-Window Decoding: Text Generation with Selective Refinement. Zeyu Tang, Zhenhao Chen, Xiangchen Song, Loka Li, Yunlong Deng, Yifan Shen, Guangyi Chen, Peter Spirtes, Kun Zhang |
| 2025 | Regress, Don't Guess: A Regression-like Loss on Number Tokens for Language Models. Jonas Zausinger, Lars Pennig, Anamarija Kozina, Sean Sdahl, Julian Sikora, Adrian Dendorfer, Timofey Kuznetsov, Mohamad Hagog, Nina Wiedemann, Kacper Chlodny, Vincent Limbach, Anna Ketteler, Thorben Prein, Vishwa Mohan Singh, Michael M. Danziger, Jannis Born |
| 2025 | Regression for the Mean: Auto-Evaluation and Inference with Few Labels through Post-hoc Regression. Benjamin Eyre, David Madras |
| 2025 | Regret-Free Reinforcement Learning for Temporal Logic Specifications. Rupak Majumdar, Mahmoud Salamati, Sadegh Soudjani |
| 2025 | Regularized Langevin Dynamics for Combinatorial Optimization. Shengyu Feng, Yiming Yang |
| 2025 | Reidentify: Context-Aware Identity Generation for Contextual Multi-Agent Reinforcement Learning. Zhiwei Xu, Kun Hu, Xin Xin, Weiliang Meng, Yiwei Shi, Hangyu Mao, Bin Zhang, Dapeng Li, Jiangjin Yin |
| 2025 | ReinboT: Amplifying Robot Visual-Language Manipulation with Reinforcement Learning. Hongyin Zhang, Zifeng Zhuang, Han Zhao, Pengxiang Ding, Hongchao Lu, Donglin Wang |
| 2025 | Reinforce LLM Reasoning through Multi-Agent Reflection. Yurun Yuan, Tengyang Xie |
| 2025 | Reinforced Learning Explicit Circuit Representations for Quantum State Characterization from Local Measurements. Manwen Liao, Yan Zhu, Weitian Zhang, Yuxiang Yang |
| 2025 | Reinforced Lifelong Editing for Language Models. Zherui Li, Houcheng Jiang, Hao Chen, Baolong Bi, Zhenhong Zhou, Fei Sun, Junfeng Fang, Xiang Wang |
| 2025 | Reinforcement Learning Control of a Physical Robot Device for Assisted Human Walking without a Simulator. Junmin Zhong, Emiliano Quiñones Yumbla, Seyed Yousef Soltanian, Ruofan Wu, Wenlong Zhang, Jennie Si |
| 2025 | Reinforcement Learning for Quantum Control under Physical Constraints. Jan Ole Ernst, Aniket Chatterjee, Tim Franzmeyer, Axel Kuhn |
| 2025 | Reinforcement Learning with Adaptive Reward Modeling for Expensive-to-Evaluate Systems. Hongyuan Su, Yu Zheng, Yuan Yuan, Yuming Lin, Depeng Jin, Yong Li |
| 2025 | Reinforcement Learning with Random Time Horizons. Enric Ribera Borrell, Lorenz Richter, Christof Schütte |
| 2025 | Reinforcement Learning with Segment Feedback. Yihan Du, Anna Winnicki, Gal Dalal, Shie Mannor, R. Srikant |
| 2025 | Rejecting Hallucinated State Targets during Planning. Harry Zhao, Tristan Sylvain, Romain Laroche, Doina Precup, Yoshua Bengio |
| 2025 | RelGNN: Composite Message Passing for Relational Deep Learning. Tianlang Chen, Charilaos I. Kanatsoulis, Jure Leskovec |
| 2025 | Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss. Abhijeet Mulgund, Chirag Pabbaraju |
| 2025 | Relational Conformal Prediction for Correlated Time Series. Andrea Cini, Alexander Jenkins, Danilo P. Mandic, Cesare Alippi, Filippo Maria Bianchi |
| 2025 | Relational Invariant Learning for Robust Solvation Free Energy Prediction. Yeyun Chen |
| 2025 | Relative Error Fair Clustering in the Weak-Strong Oracle Model. Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H.-C. Jiang, Hoai-An Nguyen, Chen Wang, Yubo Zhang, Samson Zhou |
| 2025 | Reliable Algorithm Selection for Machine Learning-Guided Design. Clara Fannjiang, Ji Won Park |
| 2025 | Reliable and Efficient Amortized Model-based Evaluation. Sang T. Truong, Yuheng Tu, Percy Liang, Bo Li, Sanmi Koyejo |
| 2025 | RepLoRA: Reparameterizing Low-rank Adaptation via the Perspective of Mixture of Experts. Tuan Truong, Chau Nguyen, Huy Nguyen, Minh Le, Trung Le, Nhat Ho |
| 2025 | RepoAudit: An Autonomous LLM-Agent for Repository-Level Code Auditing. Jinyao Guo, Chengpeng Wang, Xiangzhe Xu, Zian Su, Xiangyu Zhang |
| 2025 | Representation Preserving Multiclass Agnostic to Realizable Reduction. Steve Hanneke, Qinglin Meng, Amirreza Shaeiri |
| 2025 | Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing. Kento Nishi, Rahul Ramesh, Maya Okawa, Mikail Khona, Hidenori Tanaka, Ekdeep Singh Lubana |
| 2025 | Representation Surgery in Model Merging with Probabilistic Modeling. Qi Wei, Shuo He, Enneng Yang, Tingcong Liu, Haobo Wang, Lei Feng, Bo An |
| 2025 | Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions. Yihao Xue, Jiping Li, Baharan Mirzasoleiman |
| 2025 | Representative Language Generation. Charlotte Peale, Vinod Raman, Omer Reingold |
| 2025 | Representative Ranking for Deliberation in the Public Sphere. Manon Revel, Smitha Milli, Tyler Lu, Jamelle Watson-Daniels, Maximilian Nickel |
| 2025 | ResKoopNet: Learning Koopman Representations for Complex Dynamics with Spectral Residuals. Yuanchao Xu, Kaidi Shao, Nikos K. Logothetis, Zhongwei Shen |
| 2025 | ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals. Utkarsh Saxena, Sayeh Sharify, Kaushik Roy, Xin Wang |
| 2025 | ResearchTown: Simulator of Human Research Community. Haofei Yu, Zhaochen Hong, Zirui Cheng, Kunlun Zhu, Keyang Xuan, Jinwei Yao, Tao Feng, Jiaxuan You |
| 2025 | Residual Matrix Transformers: Scaling the Size of the Residual Stream. Brian Mak, Jeffrey Flanigan |
| 2025 | Residual TPP: A Unified Lightweight Approach for Event Stream Data Analysis. Ruoxin Yuan, Guanhua Fang |
| 2025 | Resolving Lexical Bias in Model Editing. Hammad Rizwan, Domenic Rosati, Ga Wu, Hassan Sajjad |
| 2025 | RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior. Ching Hua Lee, Chouchang Yang, Jaejin Cho, Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Yilin Shen, Hongxia Jin |
| 2025 | Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach. Jiancong Xiao, Bojian Hou, Zhanliang Wang, Ruochen Jin, Qi Long, Weijie J. Su, Li Shen |
| 2025 | Rethink GraphODE Generalization within Coupled Dynamical System. Guancheng Wan, Zijie Huang, Wanjia Zhao, Xiao Luo, Yizhou Sun, Wei Wang |
| 2025 | Rethink the Role of Deep Learning towards Large-scale Quantum Systems. Yusheng Zhao, Chi Zhang, Yuxuan Du |
| 2025 | Rethinking Addressing in Language Models via Contextualized Equivariant Positional Encoding. Jiajun Zhu, Peihao Wang, Ruisi Cai, Jason D. Lee, Pan Li, Zhangyang Wang |
| 2025 | Rethinking Aleatoric and Epistemic Uncertainty. Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth |
| 2025 | Rethinking Benign Overfitting in Two-Layer Neural Networks. Ruichen Xu, Kexin Chen |
| 2025 | Rethinking Causal Ranking: A Balanced Perspective on Uplift Model Evaluation. Minqin Zhu, Zexu Sun, Ruoxuan Xiong, Anpeng Wu, Baohong Li, Caizhi Tang, Jun Zhou, Fei Wu, Kun Kuang |
| 2025 | Rethinking Chain-of-Thought from the Perspective of Self-Training. Zongqian Wu, Baoduo Xu, Ruochen Cui, Mengmeng Zhan, Xiaofeng Zhu, Lei Feng |
| 2025 | Rethinking Confidence Scores and Thresholds in Pseudolabeling-based SSL. Harit Vishwakarma, Yi Chen, Satya Sai Srinath Namburi GNVV, Sui Jiet Tay, Ramya Korlakai Vinayak, Frederic Sala |
| 2025 | Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning. Zeyu Gan, Yun Liao, Yong Liu |
| 2025 | Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation. Shuanghao Bai, Wanqi Zhou, Pengxiang Ding, Wei Zhao, Donglin Wang, Badong Chen |
| 2025 | Rethinking Point Cloud Data Augmentation: Topologically Consistent Deformation. Jian Bi, Qianliang Wu, Xiang Li, Shuo Chen, Jianjun Qian, Lei Luo, Jian Yang |
| 2025 | Rethinking Score Distilling Sampling for 3D Editing and Generation. Xingyu Miao, Haoran Duan, Yang Long, Jungong Han |
| 2025 | Rethinking Time Encoding via Learnable Transformation Functions. Xi Chen, Yateng Tang, Jiarong Xu, Jiawei Zhang, Siwei Zhang, Sijia Peng, Xuehao Zheng, Yun Xiong |
| 2025 | Rethinking the Bias of Foundation Model under Long-tailed Distribution. Jiahao Chen, Bin Qin, Jiangmeng Li, Hao Chen, Bing Su |
| 2025 | Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective. Aojun Lu, Hangjie Yuan, Tao Feng, Yanan Sun |
| 2025 | Rethinking the Temperature for Federated Heterogeneous Distillation. Fan Qi, Daxu Shi, Chuokun Xu, Shuai Li, Changsheng Xu |
| 2025 | Retraining with Predicted Hard Labels Provably Increases Model Accuracy. Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong |
| 2025 | Retraining-free Merging of Sparse MoE via Hierarchical Clustering. I-Chun Chen, Hsu-Shen Liu, Wei-Fang Sun, Chen-Hao Chao, Yen-Chang Hsu, Chun-Yi Lee |
| 2025 | Retrieval Augmented Time Series Forecasting. Sungwon Han, Seungeon Lee, Meeyoung Cha, Sercan Ö. Arik, Jinsung Yoon |
| 2025 | Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate. Jiahe Du, Kaixiong Zhou, Xinyu Hong, Zhaozhuo Xu, Jinbo Xu, Xiao Huang |
| 2025 | Retrieval-Augmented Language Model for Knowledge-aware Protein Encoding. Jiasheng Zhang, Delvin Ce Zhang, Shuang Liang, Zhengpin Li, Rex Ying, Jie Shao |
| 2025 | Retrieval-Augmented Perception: High-resolution Image Perception Meets Visual RAG. Wenbin Wang, Yongcheng Jing, Liang Ding, Yingjie Wang, Li Shen, Yong Luo, Bo Du, Dacheng Tao |
| 2025 | Return Capping: Sample Efficient CVaR Policy Gradient Optimisation. Harry Mead, Clarissa Costen, Bruno Lacerda, Nick Hawes |
| 2025 | Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces. Henry B. Moss, Sebastian W. Ober, Tom Diethe |
| 2025 | Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks. Yixin Cheng, Hongcheng Guo, Yangming Li, Leonid Sigal |
| 2025 | ReverB-SNN: Reversing Bit of the Weight and Activation for Spiking Neural Networks. Yufei Guo, Yuhan Zhang, Jie Zhou, Xiaode Liu, Xin Tong, Yuanpei Chen, Weihang Peng, Zhe Ma |
| 2025 | Revisiting Chain-of-Thought in Code Generation: Do Language Models Need to Learn Reasoning before Coding? Renbiao Liu, Anqi Li, Chaoding Yang, Hui Sun, Ming Li |
| 2025 | Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning. Shuai Yi, Yixiong Zou, Yuhua Li, Ruixuan Li |
| 2025 | Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness. Qi He, Peiran Yu, Ziyi Chen, Heng Huang |
| 2025 | Revisiting Cooperative Off-Policy Multi-Agent Reinforcement Learning. Yueheng Li, Guangming Xie, Zongqing Lu |
| 2025 | Revisiting Differentially Private Algorithms for Decentralized Online Learning. Xiaoyu Wang, Wenhao Yang, Chang Yao, Mingli Song, Yuanyu Wan |
| 2025 | Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation. Bowen Zheng, Tianming Yang |
| 2025 | Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model. Kaito Ariu, Alexandre Proutière, Se-Young Yun |
| 2025 | Revisiting Neural Networks for Few-Shot Learning: A Zero-Cost NAS Perspective. Haidong Kang |
| 2025 | Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in sEMG Analysis. Weiyu Guo, Ziyue Qiao, Ying Sun, Yijie Xu, Hui Xiong |
| 2025 | Revisiting Non-Acyclic GFlowNets in Discrete Environments. Nikita Morozov, Ian Maksimov, Daniil Tiapkin, Sergey Samsonov |
| 2025 | Revisiting Unbiased Implicit Variational Inference. Tobias Pielok, Bernd Bischl, David Rügamer |
| 2025 | Revisiting the Predictability of Performative, Social Events. Juan Carlos Perdomo |
| 2025 | Revolve: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization. Peiyan Zhang, Haibo Jin, Leyang Hu, Xinnuo Li, Liying Kang, Man Luo, Yangqiu Song, Haohan Wang |
| 2025 | Reward Modeling with Ordinal Feedback: Wisdom of the Crowd. Shang Liu, Yu Pan, Guanting Chen, Xiaocheng Li |
| 2025 | Reward Translation via Reward Machine in Semi-Alignable MDPs. Yun Hua, Haosheng Chen, Wenhao Li, Bo Jin, Baoxiang Wang, Hongyuan Zha, Xiangfeng Wang |
| 2025 | Reward-Augmented Data Enhances Direct Preference Alignment of LLMs. Shenao Zhang, Zhihan Liu, Boyi Liu, Yufeng Zhang, Yingxiang Yang, Yongfei Liu, Liyu Chen, Tao Sun, Zhaoran Wang |
| 2025 | Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design. Masatoshi Uehara, Xingyu Su, Yulai Zhao, Xiner Li, Aviv Regev, Shuiwang Ji, Sergey Levine, Tommaso Biancalani |
| 2025 | Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs. Ziyu Ye, Rishabh Agarwal, Tianqi Liu, Rishabh Joshi, Sarmishta Velury, Quoc V. Le, Qijun Tan, Yuan Liu |
| 2025 | Reward-Guided Speculative Decoding for Efficient LLM Reasoning. Baohao Liao, Yuhui Xu, Hanze Dong, Junnan Li, Christof Monz, Silvio Savarese, Doyen Sahoo, Caiming Xiong |
| 2025 | Reward-free World Models for Online Imitation Learning. Shangzhe Li, Zhiao Huang, Hao Su |
| 2025 | Rhomboid Tiling for Geometric Graph Deep Learning. Yipeng Zhang, Longlong Li, Kelin Xia |
| 2025 | Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks. Anas Jnini, Lorenzo Breschi, Flavio Vella |
| 2025 | Riemannian Diffusion Adaptation for Distributed Optimization on Manifolds. Xiuheng Wang, Ricardo Augusto Borsoi, Cédric Richard, Ali H. Sayed |
| 2025 | Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift. Seongho Son, William Bankes, Sayak Ray Chowdhury, Brooks Paige, Ilija Bogunovic |
| 2025 | Right Time to Learn: Promoting Generalization via Bio-inspired Spacing Effect in Knowledge Distillation. Guanglong Sun, Hongwei Yan, Liyuan Wang, Qian Li, Bo Lei, Yi Zhong |
| 2025 | Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity. Arto Maranjyan, Alexander Tyurin, Peter Richtárik |
| 2025 | Risk and cross validation in ridge regression with correlated samples. Alexander B. Atanasov, Jacob A. Zavatone-Veth, Cengiz Pehlevan |
| 2025 | Risk-Sensitive Theory of Mind: Coordinating with Agents of Unknown Bias using Cumulative Prospect Theory. Mason O. Smith, Wenlong Zhang |
| 2025 | RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models. Quan Wei, Chung-Yiu Yau, Hoi-To Wai, Yang Zhao, Dongyeop Kang, Youngsuk Park, Mingyi Hong |
| 2025 | Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism. Haoyuan Cai, Zhenghao Peng, Bolei Zhou |
| 2025 | Robust Automatic Modulation Classification with Fuzzy Regularization. Xinyan Liang, Ruijie Sang, Yuhua Qian, Qian Guo, Feijiang Li, Liang Du |
| 2025 | Robust Autonomy Emerges from Self-Play. Marco Francis Cusumano-Towner, David Hafner, Alexander Hertzberg, Brody Huval, Aleksei Petrenko, Eugene Vinitsky, Erik Wijmans, Taylor W. Killian, Stuart Bowers, Ozan Sener, Philipp Krähenbühl, Vladlen Koltun |
| 2025 | Robust Conformal Outlier Detection under Contaminated Reference Data. Meshi Bashari, Matteo Sesia, Yaniv Romano |
| 2025 | Robust Consensus Anchor Learning for Efficient Multi-view Subspace Clustering. Yalan Qin, Nan Pu, Guorui Feng, Nicu Sebe |
| 2025 | Robust ML Auditing using Prior Knowledge. Jade Garcia Bourrée, Augustin Godinot, Sayan Biswas, Anne-Marie Kermarrec, Erwan Le Merrer, Gilles Trédan, Martijn de Vos, Milos Vujasinovic |
| 2025 | Robust Multi-Agent Reinforcement Learning with Stochastic Adversary. Ziyuan Zhou, Guanjun Liu, MengChu Zhou, Weiran Guo |
| 2025 | Robust Multi-bit Text Watermark with LLM-based Paraphrasers. Xiaojun Xu, Jinghan Jia, Yuanshun Yao, Yang Liu, Hang Li |
| 2025 | Robust Multimodal Large Language Models Against Modality Conflict. Zongmeng Zhang, Wengang Zhou, Jie Zhao, Houqiang Li |
| 2025 | Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs. Greyson Brothers |
| 2025 | Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization. Cheng Tang, Zhishuai Liu, Pan Xu |
| 2025 | Robust Reward Alignment via Hypothesis Space Batch Cutting. Zhixian Xie, Haode Zhang, Yizhe Feng, Wanxin Jin |
| 2025 | Robust Secure Swap: Responsible Face Swap With Persons of Interest Redaction and Provenance Traceability. Yunshu Dai, Jianwei Fei, Fangjun Huang, Chip Hong Chang |
| 2025 | Robust Sparsification via Sensitivity. Chansophea Wathanak In, Yi Li, David P. Woodruff, Xuan Wu |
| 2025 | Robust Spatio-Temporal Centralized Interaction for OOD Learning. Jiaming Ma, Binwu Wang, Pengkun Wang, Zhengyang Zhou, Xu Wang, Yang Wang |
| 2025 | Robust and Conjugate Spatio-Temporal Gaussian Processes. William Laplante, Matías Altamirano, Andrew B. Duncan, Jeremias Knoblauch, François-Xavier Briol |
| 2025 | RobustLight: Improving Robustness via Diffusion Reinforcement Learning for Traffic Signal Control. Mingyuan Li, Jiahao Wang, Guangsheng Yu, Xu Wang, Qianrun Chen, Wei Ni, Lixiang Li, Haipeng Peng |
| 2025 | RobustZero: Enhancing MuZero Reinforcement Learning Robustness to State Perturbations. Yushuai Li, Hengyu Liu, Torben Bach Pedersen, Yuqiang He, Kim Guldstrand Larsen, Lu Chen, Christian S. Jensen, Jiachen Xu, Tianyi Li |
| 2025 | RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression. Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, Alexey Tumanov |
| 2025 | Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction. Vaishnavh Nagarajan, Chen Henry Wu, Charles Ding, Aditi Raghunathan |
| 2025 | RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer. Haotian Ni, Yake Wei, Hang Liu, Gong Chen, Chong Peng, Hao Lin, Di Hu |
| 2025 | RuleAdapter: Dynamic Rules for training Safety Reward Models in RLHF. Xiaomin Li, Mingye Gao, Zhiwei Zhang, Jingxuan Fan, Weiyu Li |
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| 2025 | Rényi Neural Processes. Xuesong Wang, He Zhao, Edwin V. Bonilla |
| 2025 | S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking. Tao Tang, Lijun Zhou, Pengkun Hao, Zihang He, Kalok Ho, Shuo Gu, Zhihui Hao, Haiyang Sun, Kun Zhan, Peng Jia, Xianpeng Lang, Xiaodan Liang |
| 2025 | S2FGL: Spatial Spectral Federated Graph Learning. Zihan Tan, Suyuan Huang, Guancheng Wan, Wenke Huang, He Li, Mang Ye |
| 2025 | S4S: Solving for a Fast Diffusion Model Solver. Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh |
| 2025 | SADA: Stability-guided Adaptive Diffusion Acceleration. Ting Jiang, Yixiao Wang, Hancheng Ye, Zishan Shao, Jingwei Sun, Jingyang Zhang, Zekai Chen, Jianyi Zhang, Yiran Chen, Hai Li |
| 2025 | SAE-V: Interpreting Multimodal Models for Enhanced Alignment. Hantao Lou, Changye Li, Jiaming Ji, Yaodong Yang |
| 2025 | SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability. Adam Karvonen, Can Rager, Johnny Lin, Curt Tigges, Joseph Isaac Bloom, David Chanin, Yeu-Tong Lau, Eoin Farrell, Callum McDougall, Kola Ayonrinde, Demian Till, Matthew Wearden, Arthur Conmy, Samuel Marks, Neel Nanda |
| 2025 | SAFE: Finding Sparse and Flat Minima to Improve Pruning. Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee |
| 2025 | SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes. Yishan Shen, Yuyang Ye, Hui Xiong, Yong Chen |
| 2025 | SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation. Yuqi Fan, Zhiyong Cui, Zhenning Li, Yilong Ren, Haiyang Yu |
| 2025 | SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation. Haoquan Fang, Markus Grotz, Wilbert Pumacay, Yi Ru Wang, Dieter Fox, Ranjay Krishna, Jiafei Duan |
| 2025 | SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning. Gaole Dai, Chun-Kai Fan, Yiming Tang, Zhi Zhang, Yuan Zhang, Yulu Gan, Qizhe Zhang, Cheng-Ching Tseng, Shanghang Zhang, Tiejun Huang |
| 2025 | SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer. Enze Xie, Junsong Chen, Yuyang Zhao, Jincheng Yu, Ligeng Zhu, Yujun Lin, Zhekai Zhang, Muyang Li, Junyu Chen, Han Cai, Bingchen Liu, Daquan Zhou, Song Han |
| 2025 | SAND: One-Shot Feature Selection with Additive Noise Distortion. Pedram Pad, Hadi Hammoud, Mohamad Dia, Nadim Maamari, Liza Andrea Dunbar |
| 2025 | SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders. Bartosz Cywinski, Kamil Deja |
| 2025 | SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion. Junwei Su, Shan Wu |
| 2025 | SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval. Nikolaos Chaidos, Angeliki Dimitriou, Maria Lymperaiou, Giorgos Stamou |
| 2025 | SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields. David Keetae Park, Xihaier Luo, Guang Zhao, Seungjun Lee, Miruna Oprescu, Shinjae Yoo |
| 2025 | SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification. Shuo Yang, Bardh Prenkaj, Gjergji Kasneci |
| 2025 | SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations. Grigory Bartosh, Dmitry P. Vetrov, Christian A. Naesseth |
| 2025 | SDMG: Smoothing Your Diffusion Models for Powerful Graph Representation Learning. Junyou Zhu, Langzhou He, Chao Gao, Dongpeng Hou, Zhen Su, Philip S. Yu, Jürgen Kurths, Frank Hellmann |
| 2025 | SDP-CROWN: Efficient Bound Propagation for Neural Network Verification with Tightness of Semidefinite Programming. Hong-Ming Chiu, Hao Chen, Huan Zhang, Richard Y. Zhang |
| 2025 | SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space. Xupeng Zhu, Fan Wang, Robin Walters, Jane Shi |
| 2025 | SEAD: Unsupervised Ensemble of Streaming Anomaly Detectors. Saumya Gaurang Shah, Abishek Sankararaman, Balakrishnan Narayanaswamy, Vikramank Y. Singh |
| 2025 | SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding. Woohyeon Park, Woojin Kim, Jaeik Kim, Jaeyoung Do |
| 2025 | SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning. Jinpeng Chen, Runmin Cong, Yuzhi Zhao, Hongzheng Yang, Guangneng Hu, Horace H. S. Ip, Sam Kwong |
| 2025 | SEMU: Singular Value Decomposition for Efficient Machine Unlearning. Marcin Sendera, Lukasz Struski, Kamil Ksiazek, Kryspin Musiol, Jacek Tabor, Dawid Damian Rymarczyk |
| 2025 | SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models. Cansu Sancaktar, Christian Gumbsch, Andrii Zadaianchuk, Pavel Kolev, Georg Martius |
| 2025 | SERENA: A Unified Stochastic Recursive Variance Reduced Gradient Framework for Riemannian Non-Convex Optimization. Yan Liu, Mingjie Chen, Chaojie Ji, Hao Zhang, Ruxin Wang |
| 2025 | SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training. Tianzhe Chu, Yuexiang Zhai, Jihan Yang, Shengbang Tong, Saining Xie, Dale Schuurmans, Quoc V. Le, Sergey Levine, Yi Ma |
| 2025 | SGD Jittering: A Training Strategy for Robust and Accurate Model-Based Architectures. Peimeng Guan, Mark A. Davenport |
| 2025 | SHARP-Distill: A 68× Faster Recommender System with Hypergraph Neural Networks and Language Models. Saman Forouzandeh, Parham Moradi, Mahdi Jalili |
| 2025 | SHE: Streaming-media Hashing Retrieval. Ruitao Pu, Yang Qin, Xiaomin Song, Dezhong Peng, Zhenwen Ren, Yuan Sun |
| 2025 | SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchy. Yong Liang Goh, Zhiguang Cao, Yining Ma, Jianan Zhou, Mohammed Haroon Dupty, Wee Sun Lee |
| 2025 | SIMPLEMIX: Frustratingly Simple Mixing of Off- and On-policy Data in Language Model Preference Learning. Tianjian Li, Daniel Khashabi |
| 2025 | SING: Spatial Context in Large Language Model for Next-Gen Wearables. Ayushi Mishra, Yang Bai, Priyadarshan Narayanasamy, Nakul Garg, Nirupam Roy |
| 2025 | SITCOM: Step-wise Triple-Consistent Diffusion Sampling For Inverse Problems. Ismail Alkhouri, Shijun Liang, Cheng-Han Huang, Jimmy Dai, Qing Qu, Saiprasad Ravishankar, Rongrong Wang |
| 2025 | SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs. Xin Su, Man Luo, Kris W. Pan, Tien Pei Chou, Vasudev Lal, Phillip Howard |
| 2025 | SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization. Runsheng Bai, Bo Liu, Qiang Liu |
| 2025 | SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting. Yitian Zhang, Liheng Ma, Antonios Valkanas, Boris N. Oreshkin, Mark Coates |
| 2025 | SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression. Mohammad Mozaffari, Amir Yazdanbakhsh, Maryam Mehri Dehnavi |
| 2025 | SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds. Ali Bahri, Moslem Yazdanpanah, Sahar Dastani, Mehrdad Noori, Gustavo Adolfo Vargas Hakim, David Osowiechi, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers |
| 2025 | SNS-Bench: Defining, Building, and Assessing Capabilities of Large Language Models in Social Networking Services. Hongcheng Guo, Yue Wang, Shaosheng Cao, Fei Zhao, Boyang Wang, Lei Li, Liang Chen, Xinze Lyu, Zhe Xu, Yao Hu, Zhoujun Li |
| 2025 | SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels. Malte Mosbach, Jan Niklas Ewertz, Angel Villar-Corrales, Sven Behnke |
| 2025 | SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model. Zhao Yang, Jiwei Zhu, Bing Su |
| 2025 | SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models. Han-Byul Kim, Duc N. M. Hoang, Arnav Kundu, Mohammad Samragh, Minsik Cho |
| 2025 | SPEX: Scaling Feature Interaction Explanations for LLMs. Justin Singh Kang, Landon Butler, Abhineet Agarwal, Yigit Efe Erginbas, Ramtin Pedarsani, Bin Yu, Kannan Ramchandran |
| 2025 | SPHINX: Structural Prediction using Hypergraph Inference Network. Iulia Duta, Pietro Lio |
| 2025 | SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution. Wenwen He, Wenke Huang, Bin Yang, Shukan Liu, Mang Ye |
| 2025 | SPRI: Aligning Large Language Models with Context-Situated Principles. Hongli Zhan, Muneeza Azmat, Raya Horesh, Junyi Jessy Li, Mikhail Yurochkin |
| 2025 | SSHR: More Secure Generative Steganography with High-Quality Revealed Secret Images. Jiannian Wang, Yao Lu, Guangming Lu |
| 2025 | STAIR: Improving Safety Alignment with Introspective Reasoning. Yichi Zhang, Siyuan Zhang, Yao Huang, Zeyu Xia, Zhengwei Fang, Xiao Yang, Ranjie Duan, Dong Yan, Yinpeng Dong, Jun Zhu |
| 2025 | STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings. Saksham Rastogi, Pratyush Maini, Danish Pruthi |
| 2025 | STAR: Learning Diverse Robot Skill Abstractions through Rotation-Augmented Vector Quantization. Hao Li, Qi Lv, Rui Shao, Xiang Deng, Yinchuan Li, Jianye Hao, Liqiang Nie |
| 2025 | STD-FD: Spatio-Temporal Distribution Fitting Deviation for AIGC Forgery Identification. Hengrui Lou, Zunlei Feng, Jinsong Geng, Erteng Liu, Jie Lei, Lechao Cheng, Jie Song, Mingli Song, Yijun Bei |
| 2025 | STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving. Kefan Dong, Tengyu Ma |
| 2025 | SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics. Suyuan Zhao, Yizhen Luo, Ganbo Yang, Yan Zhong, Hao Zhou, Zaiqing Nie |
| 2025 | SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics. Qingtian Zhu, Yumin Zheng, Yuling Sang, Yifan Zhan, Ziyan Zhu, Jun Ding, Yinqiang Zheng |
| 2025 | SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training. Chao Ma, Wenbo Gong, Meyer Scetbon, Edward Meeds |
| 2025 | SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? Samuel Miserendino, Michele Wang, Tejal Patwardhan, Johannes Heidecke |
| 2025 | Sable: a Performant, Efficient and Scalable Sequence Model for MARL. Omayma Mahjoub, Sasha Abramowitz, Ruan John de Kock, Wiem Khlifi, Simon du Toit, Jemma Daniel, Louay Ben Nessir, Louise Beyers, Juan Claude Formanek, Liam Clark, Arnu Pretorius |
| 2025 | Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets. Ning Lu, Shengcai Liu, Jiahao Wu, Weiyu Chen, Zhirui Zhang, Yew-Soon Ong, Qi Wang, Ke Tang |
| 2025 | Safe-EF: Error Feedback for Non-smooth Constrained Optimization. Rustem Islamov, Yarden As, Ilyas Fatkhullin |
| 2025 | SafeArena: Evaluating the Safety of Autonomous Web Agents. Ada Defne Tur, Nicholas Meade, Xing Han Lù, Alejandra Zambrano, Arkil Patel, Esin Durmus, Spandana Gella, Karolina Stanczak, Siva Reddy |
| 2025 | SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models. Jiawei Zhang, Xuan Yang, Taiqi Wang, Yu Yao, Aleksandr Petiushko, Bo Li |
| 2025 | SafeMap: Robust HD Map Construction from Incomplete Observations. Xiaoshuai Hao, Lingdong Kong, Rong Yin, Pengwei Wang, Jing Zhang, Yunfeng Diao, Shu Zhao |
| 2025 | Safely Learning Optimal Auctions: A Testable Learning Framework for Mechanism Design. Vikram Kher, Manolis Zampetakis |
| 2025 | Safety Alignment Can Be Not Superficial With Explicit Safety Signals. Jianwei Li, Jung-Eun Kim |
| 2025 | Safety Certificate against Latent Variables with Partially Unidentifiable Dynamics. Haoming Jing, Yorie Nakahira |
| 2025 | Safety Reasoning with Guidelines. Haoyu Wang, Zeyu Qin, Li Shen, Xueqian Wang, Dacheng Tao, Minhao Cheng |
| 2025 | Safety-Polarized and Prioritized Reinforcement Learning. Ke Fan, Jinpeng Zhang, Xuefeng Zhang, Yunze Wu, Jingyu Cao, Yuan Zhou, Jianzhu Ma |
| 2025 | SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior. Jing-Jing Li, Valentina Pyatkin, Max Kleiman-Weiner, Liwei Jiang, Nouha Dziri, Anne Collins, Jana Schaich Borg, Maarten Sap, Yejin Choi, Sydney Levine |
| 2025 | SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization. Jintao Zhang, Haofeng Huang, Pengle Zhang, Jia Wei, Jun Zhu, Jianfei Chen |
| 2025 | Sample Complexity of Branch-length Estimation by Maximum Likelihood. David Clancy Jr., Hanbaek Lyu, Sebastien Roch |
| 2025 | Sample Complexity of Correlation Detection in the Gaussian Wigner Model. Dong Huang, Pengkun Yang |
| 2025 | Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction. Yiting He, Zhishuai Liu, Weixin Wang, Pan Xu |
| 2025 | Sample Efficient Demonstration Selection for In-Context Learning. Kiran Purohit, Venktesh V, Sourangshu Bhattacharya, Avishek Anand |
| 2025 | Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification. Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi |
| 2025 | Sample-Optimal Agnostic Boosting with Unlabeled Data. Udaya Ghai, Karan Singh |
| 2025 | Sample-specific Noise Injection for Diffusion-based Adversarial Purification. Yuhao Sun, Jiacheng Zhang, Zesheng Ye, Chaowei Xiao, Feng Liu |
| 2025 | Sampling Binary Data by Denoising through Score Functions. Francis Bach, Saeed Saremi |
| 2025 | Sampling from Binary Quadratic Distributions via Stochastic Localization. Chenguang Wang, Kaiyuan Cui, Weichen Zhao, Tianshu Yu |
| 2025 | Sanity Checking Causal Representation Learning on a Simple Real-World System. Juan L. Gamella, Simon Bing, Jakob Runge |
| 2025 | Sassha: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation. Dahun Shin, Dongyeop Lee, Jinseok Chung, Namhoon Lee |
| 2025 | Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search. Maohao Shen, Guangtao Zeng, Zhenting Qi, Zhang-Wei Hong, Zhenfang Chen, Wei Lu, Gregory W. Wornell, Subhro Das, David Daniel Cox, Chuang Gan |
| 2025 | Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. Paul Mangold, Alain Oliviero Durmus, Aymeric Dieuleveut, Eric Moulines |
| 2025 | Scalable Approximation Algorithms for p-Wasserstein Distance and Its Variants. Nathaniel Lahn, Sharath Raghvendra, Emma Saarinen, Pouyan Shirzadian |
| 2025 | Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation. Yaowen Hu, Wenxuan Tu, Yue Liu, Xinhang Wan, Junyi Yan, Taichun Zhou, Xinwang Liu |
| 2025 | Scalable Equilibrium Sampling with Sequential Boltzmann Generators. Charlie B. Tan, Joey Bose, Chen Lin, Leon Klein, Michael M. Bronstein, Alexander Tong |
| 2025 | Scalable First-order Method for Certifying Optimal k-Sparse GLMs. Jiachang Liu, Soroosh Shafiee, Andrea Lodi |
| 2025 | Scalable Gaussian Processes with Latent Kronecker Structure. Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy |
| 2025 | Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching. Tinglin Huang, Tianyu Liu, Mehrtash Babadi, Wengong Jin, Rex Ying |
| 2025 | Scalable Meta-Learning via Mixed-Mode Differentiation. Iurii Kemaev, Dan A. Calian, Luisa M. Zintgraf, Gregory Farquhar, Hado van Hasselt |
| 2025 | Scalable Model Merging with Progressive Layer-wise Distillation. Jing Xu, Jiazheng Li, Jingzhao Zhang |
| 2025 | Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment. Yuhui Ding, Thomas Hofmann |
| 2025 | Scalable Private Partition Selection via Adaptive Weighting. Justin Y. Chen, Vincent Cohen-Addad, Alessandro Epasto, Morteza Zadimoghaddam |
| 2025 | Scalable Sobolev IPM for Probability Measures on a Graph. Tam Le, Truyen Nguyen, Hideitsu Hino, Kenji Fukumizu |
| 2025 | Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks. Shikai Qiu, Lechao Xiao, Andrew Gordon Wilson, Jeffrey Pennington, Atish Agarwala |
| 2025 | Scaling Inference-Efficient Language Models. Song Bian, Minghao Yan, Shivaram Venkataraman |
| 2025 | Scaling Large Motion Models with Million-Level Human Motions. Ye Wang, Sipeng Zheng, Bin Cao, Qianshan Wei, Weishuai Zeng, Qin Jin, Zongqing Lu |
| 2025 | Scaling Laws for Differentially Private Language Models. Ryan McKenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, Georgios Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, Chiyuan Zhang |
| 2025 | Scaling Laws for Floating-Point Quantization Training. Xingwu Sun, Shuaipeng Li, Ruobing Xie, Weidong Han, Kan Wu, Zhen Yang, Yixing Li, An Wang, Shuai Li, Jinbao Xue, Yu Cheng, Yangyu Tao, Zhanhui Kang, Cheng-Zhong Xu, Di Wang, Jie Jiang |
| 2025 | Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection. Louis Béthune, David Grangier, Dan Busbridge, Eleonora Gualdoni, Marco Cuturi, Pierre Ablin |
| 2025 | Scaling Laws for Pre-training Agents and World Models. Tim Pearce, Tabish Rashid, David Bignell, Raluca Georgescu, Sam Devlin, Katja Hofmann |
| 2025 | Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream. Abdülkadir Gökce, Martin Schrimpf |
| 2025 | Scaling Laws for Upcycling Mixture-of-Experts Language Models. Seng Pei Liew, Takuya Kato, Sho Takase |
| 2025 | Scaling Laws in Patchification: An Image Is Worth 50, 176 Tokens And More. Feng Wang, Yaodong Yu, Wei Shao, Yuyin Zhou, Alan L. Yuille, Cihang Xie |
| 2025 | Scaling Probabilistic Circuits via Monarch Matrices. Honghua Zhang, Meihua Dang, Benjie Wang, Stefano Ermon, Nanyun Peng, Guy Van den Broeck |
| 2025 | Scaling Sparse Feature Circuits For Studying In-Context Learning. Dmitrii Kharlapenko, Stepan Shabalin, Arthur Conmy, Neel Nanda |
| 2025 | Scaling Test-Time Compute Without Verification or RL is Suboptimal. Amrith Setlur, Nived Rajaraman, Sergey Levine, Aviral Kumar |
| 2025 | Scaling Trends in Language Model Robustness. Nikolaus H. R. Howe, Ian R. McKenzie, Oskar John Hollinsworth, Michal Zajac, Tom Tseng, Aaron David Tucker, Pierre-Luc Bacon, Adam Gleave |
| 2025 | Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning. Yuhui Wang, Qingyuan Wu, Dylan R. Ashley, Francesco Faccio, Weida Li, Chao Huang, Jürgen Schmidhuber |
| 2025 | Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation. Chuanqi Cheng, Jian Guan, Wei Wu, Rui Yan |
| 2025 | Schwarz-Schur Involution: Lightspeed Differentiable Sparse Linear Solvers. Yu Wang, S. Mazdak Abulnaga, Yaël Balbastre, Bruce Fischl |
| 2025 | Score Matching with Missing Data. Josh Givens, Song Liu, Henry W. J. Reeve |
| 2025 | Score as Action: Fine Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning. Hanyang Zhao, Haoxian Chen, Ji Zhang, David D. Yao, Wenpin Tang |
| 2025 | Score-Based Diffusion Policy Compatible with Reinforcement Learning via Optimal Transport. Mingyang Sun, Pengxiang Ding, Weinan Zhang, Donglin Wang |
| 2025 | Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows. Willem Diepeveen, Georgios Batzolis, Zakhar Shumaylov, Carola-Bibiane Schönlieb |
| 2025 | Score-of-Mixture Training: One-Step Generative Model Training Made Simple via Score Estimation of Mixture Distributions. Tejas Jayashankar, Jongha Jon Ryu, Gregory W. Wornell |
| 2025 | SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding. Peihua Mai, Youlong Ding, Ziyan Lyu, Minxin Du, Yan Pang |
| 2025 | Secant Line Search for Frank-Wolfe Algorithms. Deborah Hendrych, Sebastian Pokutta, Mathieu Besançon, David Martínez-Rubio |
| 2025 | Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games. Jiawei Ge, Yuanhao Wang, Wenzhe Li, Chi Jin |
| 2025 | SeedLoRA: A Fusion Approach to Efficient LLM Fine-Tuning. Yong Liu, Di Fu, Shenggan Cheng, Zirui Zhu, Yang Luo, Minhao Cheng, Cho-Jui Hsieh, Yang You |
| 2025 | Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation. Zhihua Liu, Amrutha Saseendran, Lei Tong, Xilin He, Fariba Yousefi, Nikolay Burlutskiy, Dino Oglic, Tom Diethe, Philip Alexander Teare, Huiyu Zhou, Chen Jin |
| 2025 | Selective Preference Aggregation. Shreyas Kadekodi, Hayden McTavish, Berk Ustun |
| 2025 | Selective Prompt Anchoring for Code Generation. Yuan Tian, Tianyi Zhang |
| 2025 | Selective Response Strategies for GenAI. Boaz Taitler, Omer Ben-Porat |
| 2025 | Self-Bootstrapping for Versatile Test-Time Adaptation. Shuaicheng Niu, Guohao Chen, Peilin Zhao, Tianyi Wang, Pengcheng Wu, Zhiqi Shen |
| 2025 | Self-Consistency Preference Optimization. Archiki Prasad, Weizhe Yuan, Richard Yuanzhe Pang, Jing Xu, Maryam Fazel-Zarandi, Mohit Bansal, Sainbayar Sukhbaatar, Jason E. Weston, Jane Yu |
| 2025 | Self-Consuming Generative Models with Adversarially Curated Data. Xiukun Wei, Xueru Zhang |
| 2025 | Self-Discriminative Modeling for Anomalous Graph Detection. Jinyu Cai, Yunhe Zhang, Jicong Fan |
| 2025 | Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation. Jintao Tong, Yixiong Zou, Guangyao Chen, Yuhua Li, Ruixuan Li |
| 2025 | Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI. Julien Pourcel, Cédric Colas, Pierre-Yves Oudeyer |
| 2025 | Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges. Nayoung Lee, Ziyang Cai, Avi Schwarzschild, Kangwook Lee, Dimitris Papailiopoulos |
| 2025 | Self-Organizing Visual Prototypes for Non-Parametric Representation Learning. Thalles Silva, Hélio Pedrini, Adín Ramírez Rivera |
| 2025 | Self-Play Q-Learners Can Provably Collude in the Iterated Prisoner's Dilemma. Quentin Bertrand, Juan Agustin Duque, Emilio Calvano, Gauthier Gidel |
| 2025 | Self-Supervised Learning of Intertwined Content and Positional Features for Object Detection. Kang-Jun Liu, Masanori Suganuma, Takayuki Okatani |
| 2025 | Self-Supervised Transformers as Iterative Solution Improvers for Constraint Satisfaction. Yudong Xu, Wenhao Li, Scott Sanner, Elias Boutros Khalil |
| 2025 | Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning. Qi Xu, Junyang Zhu, Dongdong Zhou, Hao Chen, Yang Liu, Jiangrong Shen, Qiang Zhang |
| 2025 | Self-supervised Adversarial Purification for Graph Neural Networks. Woohyun Lee, Hogun Park |
| 2025 | Self-supervised Masked Graph Autoencoder via Structure-aware Curriculum. Haoyang Li, Xin Wang, Zeyang Zhang, Zongyuan Wu, Linxin Xiao, Wenwu Zhu |
| 2025 | SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models. Yung-Sung Chuang, Benjamin Cohen-Wang, Zejiang Shen, Zhaofeng Wu, Hu Xu, Xi Victoria Lin, James R. Glass, Shang-wen Li, Wen-tau Yih |
| 2025 | Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning. Run He, Di Fang, Yicheng Xu, Yawen Cui, Ming Li, Cen Chen, Ziqian Zeng, Huiping Zhuang |
| 2025 | Semantics-aware Test-time Adaptation for 3D Human Pose Estimation. Qiuxia Lin, Rongyu Chen, Kerui Gu, Angela Yao |
| 2025 | Semi-Supervised Blind Quality Assessment with Confidence-quantifiable Pseudo-label Learning for Authentic Images. Yan Zhong, Chenxi Yang, Suyuan Zhao, Tingting Jiang |
| 2025 | SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator. Guoxuan Chen, Han Shi, Jiawei Li, Yihang Gao, Xiaozhe Ren, Yimeng Chen, Xin Jiang, Zhenguo Li, Weiyang Liu, Chao Huang |
| 2025 | Separating Knowledge and Perception with Procedural Data. Adrián Rodríguez-Muñoz, Manel Baradad, Phillip Isola, Antonio Torralba |
| 2025 | Set Valued Predictions For Robust Domain Generalization. Ron Tsibulsky, Daniel Nevo, Uri Shalit |
| 2025 | Settling the Maximin Share Fairness for Scheduling among Groups of Machines. Bo Li, Fangxiao Wang, Shiji Xing |
| 2025 | ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference. Hanshi Sun, Li-Wen Chang, Wenlei Bao, Size Zheng, Ningxin Zheng, Xin Liu, Harry Dong, Yuejie Chi, Beidi Chen |
| 2025 | Sharp Generalization for Nonparametric Regression by Over-Parameterized Neural Networks: A Distribution-Free Analysis in Spherical Covariate. Yingzhen Yang |
| 2025 | Sharp Optimality of Simple, Plug-in Estimation of the Fisher Information of a Smoothed Density. Subhodh Kotekal |
| 2025 | ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning. Zhaorun Chen, Mintong Kang, Bo Li |
| 2025 | Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency. Michael Kirchhof, James Thornton, Louis Béthune, Pierre Ablin, Eugène Ndiaye, Marco Cuturi |
| 2025 | Shifting Time: Time-series Forecasting with Khatri-Rao Neural Operators. Srinath Dama, Kevin Course, Prasanth B. Nair |
| 2025 | Shortcut-connected Expert Parallelism for Accelerating Mixture of Experts. Weilin Cai, Juyong Jiang, Le Qin, Junwei Cui, Sunghun Kim, Jiayi Huang |
| 2025 | Should Decision-Makers Reveal Classifiers in Online Strategic Classification? Han Shao, Shuo Xie, Kunhe Yang |
| 2025 | Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN. Talal Widatalla, Richard W. Shuai, Brian Hie, Possu Huang |
| 2025 | Signed Laplacians for Constrained Graph Clustering. John Stewart Fabila-Carrasco, He Sun |
| 2025 | Simple Path Structural Encoding for Graph Transformers. Louis Airale, Antonio Longa, Mattia Rigon, Andrea Passerini, Roberto Passerone |
| 2025 | Simple Policy Optimization. Zhengpeng Xie, Qiang Zhang, Fan Yang, Marco Hutter, Renjing Xu |
| 2025 | Simple Randomized Rounding for Max-Min Eigenvalue Augmentation. Jourdain B. Lamperski, Haeseong Yang, Oleg A. Prokopyev |
| 2025 | Simple and Critical Iterative Denoising: A Recasting of Discrete Diffusion in Graph Generation. Yoann Boget |
| 2025 | Simplicity Bias and Optimization Threshold in Two-Layer ReLU Networks. Etienne Boursier, Nicolas Flammarion |
| 2025 | Simplifying DINO via Coding Rate Regularization. Ziyang Wu, Jingyuan Zhang, Druv Pai, Xudong Wang, Chandan Singh, Jianwei Yang, Jianfeng Gao, Yi Ma |
| 2025 | Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models. Jinhao Liang, Jacob K. Christopher, Sven Koenig, Ferdinando Fioretto |
| 2025 | Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery. Mateusz Olko, Mateusz Gajewski, Joanna Wojciechowska, Mikolaj Morzy, Piotr Sankowski, Piotr Milos |
| 2025 | Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation. Tianyi Zhang, Junda Su, Aditya Desai, Oscar Wu, Zhaozhuo Xu, Anshumali Shrivastava |
| 2025 | SketchDNN: Joint Continuous-Discrete Diffusion for CAD Sketch Generation. Sathvik Chereddy, John Femiani |
| 2025 | Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data. Krzysztof Kacprzyk, Julianna Piskorz, Mihaela van der Schaar |
| 2025 | SkipGPT: Each Token is One of a Kind. Anhao Zhao, Fanghua Ye, Yingqi Fan, Junlong Tong, Jing Xiong, Zhiwei Fei, Hui Su, Xiaoyu Shen |
| 2025 | Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation. Hoigi Seo, Wongi Jeong, Jae-sun Seo, Se Young Chun |
| 2025 | Sleeping Reinforcement Learning. Simone Drago, Marco Mussi, Alberto Maria Metelli |
| 2025 | SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models. Wei Huang, Haotong Qin, Yangdong Liu, Yawei Li, Qinshuo Liu, Xianglong Liu, Luca Benini, Michele Magno, Shiming Zhang, Xiaojuan Qi |
| 2025 | Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning. Bryan Lincoln Marques de Oliveira, Luana Guedes Barros Martins, Bruno Brandão, Murilo Lopes da Luz, Telma Woerle de Lima Soares, Luckeciano Carvalho Melo |
| 2025 | SlimLLM: Accurate Structured Pruning for Large Language Models. Jialong Guo, Xinghao Chen, Yehui Tang, Yunhe Wang |
| 2025 | Slimming the Fat-Tail: Morphing-Flow for Adaptive Time Series Modeling. Tianyu Liu, Kai Sun, Fuchun Sun, Yu Luo, Yuanlong Zhang |
| 2025 | Smooth Interpolation for Improved Discrete Graph Generative Models. Yuxuan Song, Juntong Shi, Jingjing Gong, Minkai Xu, Stefano Ermon, Hao Zhou, Wei-Ying Ma |
| 2025 | Smoothed Preference Optimization via ReNoise Inversion for Aligning Diffusion Models with Varied Human Preferences. Yunhong Lu, Qichao Wang, Hengyuan Cao, Xiaoyin Xu, Min Zhang |
| 2025 | Socialized Coevolution: Advancing a Better World through Cross-Task Collaboration. Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu |
| 2025 | Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration. Qinglin Zhu, Runcong Zhao, Hanqi Yan, Yulan He, Yudong Chen, Lin Gui |
| 2025 | Softmax is not Enough (for Sharp Size Generalisation). Petar Velickovic, Christos Perivolaropoulos, Federico Barbero, Razvan Pascanu |
| 2025 | Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo. Filip Ekström Kelvinius, Zheng Zhao, Fredrik Lindsten |
| 2025 | Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound. David Boetius, Stefan Leue, Tobias Sutter |
| 2025 | Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits. Jinzhao Li, Nan Jiang, Yexiang Xue |
| 2025 | Solving Zero-Sum Convex Markov Games. Fivos Kalogiannis, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Ian Gemp, Georgios Piliouras |
| 2025 | SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation. Zihan Liu, Shuangrui Ding, Zhixiong Zhang, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang |
| 2025 | Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model. Kaiwen Tang, Zhanglu Yan, Weng-Fai Wong |
| 2025 | Sort Before You Prune: Improved Worst-Case Guarantees of the DiskANN Family of Graphs. Siddharth Gollapudi, Ravishankar Krishnaswamy, Kirankumar Shiragur, Harsh Wardhan |
| 2025 | Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systems. Taejin Park, Ivan Medennikov, Kunal Dhawan, Weiqing Wang, He Huang, Nithin Rao Koluguri, Krishna C. Puvvada, Jagadeesh Balam, Boris Ginsburg |
| 2025 | Sounding that Object: Interactive Object-Aware Image to Audio Generation. Tingle Li, Baihe Huang, Xiaobin Zhuang, Dongya Jia, Jiawei Chen, Yuping Wang, Zhuo Chen, Gopala Anumanchipalli, Yuxuan Wang |
| 2025 | Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging. Pierre Ablin, Angelos Katharopoulos, Skyler Seto, David Grangier |
| 2025 | SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference. Jintao Zhang, Chendong Xiang, Haofeng Huang, Jia Wei, Haocheng Xi, Jun Zhu, Jianfei Chen |
| 2025 | Sparse Autoencoders for Hypothesis Generation. Rajiv Movva, Kenny Peng, Nikhil Garg, Jon M. Kleinberg, Emma Pierson |
| 2025 | Sparse Autoencoders, Again? Yin Lu, Xuening Zhu, Tong He, David Wipf |
| 2025 | Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation. Junyu Luo, Yuhao Tang, Yiwei Fu, Xiao Luo, Zhizhuo Kou, Zhiping Xiao, Wei Ju, Wentao Zhang, Ming Zhang |
| 2025 | Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks. Jialin Zhao, Yingtao Zhang, Xinghang Li, Huaping Liu, Carlo Vittorio Cannistraci |
| 2025 | Sparse Training from Random Initialization: Aligning Lottery Ticket Masks using Weight Symmetry. Mohammed Adnan, Rohan Jain, Ekansh Sharma, Rahul Krishnan, Yani Ioannou |
| 2025 | Sparse Video-Gen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity. Haocheng Xi, Shuo Yang, Yilong Zhao, Chenfeng Xu, Muyang Li, Xiuyu Li, Yujun Lin, Han Cai, Jintao Zhang, Dacheng Li, Jianfei Chen, Ion Stoica, Kurt Keutzer, Song Han |
| 2025 | Sparse-pivot: Dynamic correlation clustering for node insertions. Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrovic |
| 2025 | SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity. Samir Khaki, Xiuyu Li, Junxian Guo, Ligeng Zhu, Konstantinos N. Plataniotis, Amir Yazdanbakhsh, Kurt Keutzer, Song Han, Zhijian Liu |
| 2025 | SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference. Yuan Zhang, Chun-Kai Fan, Junpeng Ma, Wenzhao Zheng, Tao Huang, Kuan Cheng, Denis A. Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, Shanghang Zhang |
| 2025 | Sparsing Law: Towards Large Language Models with Greater Activation Sparsity. Yuqi Luo, Chenyang Song, Xu Han, Yingfa Chen, Chaojun Xiao, Xiaojun Meng, Liqun Deng, Jiansheng Wei, Zhiyuan Liu, Maosong Sun |
| 2025 | Spatial Reasoning with Denoising Models. Christopher Wewer, Bartlomiej Pogodzinski, Bernt Schiele, Jan Eric Lenssen |
| 2025 | SpeCache: Speculative Key-Value Caching for Efficient Generation of LLMs. Shibo Jie, Yehui Tang, Kai Han, Zhi-Hong Deng, Jing Han |
| 2025 | Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions. Yik Siu Chan, Narutatsu Ri, Yuxin Xiao, Marzyeh Ghassemi |
| 2025 | Spectral-Aware Reservoir Computing for Fast and Accurate Time Series Classification. Shikang Liu, Chuyang Wei, Xiren Zhou, Huanhuan Chen |
| 2025 | Speculate, then Collaborate: Fusing Knowledge of Language Models during Decoding. Ziyao Wang, Muneeza Azmat, Ang Li, Raya Horesh, Mikhail Yurochkin |
| 2025 | Speculative Prefill: Turbocharging TTFT with Lightweight and Training-Free Token Importance Estimation. Jingyu Liu, Beidi Chen, Ce Zhang |
| 2025 | Speeding up Policy Simulation in Supply Chain RL. Vivek F. Farias, Joren Gijsbrechts, Aryan I. Khojandi, Tianyi Peng, Andrew Zheng |
| 2025 | Spherical-Nested Diffusion Model for Panoramic Image Outpainting. Xiancheng Sun, Senmao Ma, Shengxi Li, Mai Xu, Jingyuan Xia, Lai Jiang, Xin Deng, Jiali Wang |
| 2025 | SpikF: Spiking Fourier Network for Efficient Long-term Prediction. Wenjie Wu, Dexuan Huo, Hong Chen |
| 2025 | SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and O(T) Complexity. Shihao Zou, Qingfeng Li, Wei Ji, Jingjing Li, Yongkui Yang, Guoqi Li, Chao Dong |
| 2025 | Splitting & Integrating: Out-of-Distribution Detection via Adversarial Gradient Attribution. Jiayu Zhang, Xinyi Wang, Zhibo Jin, Zhiyu Zhu, Jianlong Zhou, Fang Chen, Huaming Chen |
| 2025 | Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models. Yangxu Liao, Wenke Huang, Guancheng Wan, Jian Liang, Bin Yang, Mang Ye |
| 2025 | Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization. Simone Bombari, Marco Mondelli |
| 2025 | SquareχPO: Differentially Private and Robust χ2-Preference Optimization in Offline Direct Alignment. Xingyu Zhou, Yulian Wu, Wenqian Weng, Francesco Orabona |
| 2025 | Stability and Generalization Analysis of Decentralized SGD: Sharper Bounds Beyond Lipschitzness and Smoothness. Shuang Zeng, Yunwen Lei |
| 2025 | Stability and Generalization Capability of Subgraph Reasoning Models for Inductive Knowledge Graph Completion. Minsung Hwang, Jaejun Lee, Joyce Jiyoung Whang |
| 2025 | Stabilizing Sample Similarity in Representation via Mitigating Random Consistency. Jieting Wang, Zelong Zhang, Feijiang Li, Yuhua Qian, Xinyan Liang |
| 2025 | Stable Fair Graph Representation Learning with Lipschitz Constraint. Qiang Chen, Zhongze Wu, Xiu Su, Xi Lin, Zhe Qu, Shan You, Shuo Yang, Chang Xu |
| 2025 | Stable Offline Value Function Learning with Bisimulation-based Representations. Brahma S. Pavse, Yudong Chen, Qiaomin Xie, Josiah P. Hanna |
| 2025 | Stacey: Promoting Stochastic Steepest Descent via Accelerated ℓp-Smooth Nonconvex Optimization. Xinyu Luo, Site Bai, Bolian Li, Petros Drineas, Ruqi Zhang, Brian Bullins |
| 2025 | Staged and Physics-Grounded Learning Framework with Hyperintensity Prior for Pre-Contrast MRI Synthesis. Dayang Wang, Srivathsa Pasumarthi, Ajit Shankaranarayanan, Greg Zaharchuk |
| 2025 | Star Attention: Efficient LLM Inference over Long Sequences. Shantanu Acharya, Fei Jia, Boris Ginsburg |
| 2025 | Statistical Collusion by Collectives on Learning Platforms. Etienne Gauthier, Francis Bach, Michael I. Jordan |
| 2025 | Statistical Hypothesis Testing for Auditing Robustness in Language Models. Paulius Rauba, Qiyao Wei, Mihaela van der Schaar |
| 2025 | Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise. Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos |
| 2025 | Statistical Test for Feature Selection Pipelines by Selective Inference. Tomohiro Shiraishi, Tatsuya Matsukawa, Shuichi Nishino, Ichiro Takeuchi |
| 2025 | Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances. Jie Wang, March Boedihardjo, Yao Xie |
| 2025 | Stay Hungry, Keep Learning: Sustainable Plasticity for Deep Reinforcement Learning. Huaicheng Zhou, Zifeng Zhuang, Donglin Wang |
| 2025 | Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection. Anirudh Sundara Rajan, Yong Jae Lee |
| 2025 | Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning. Brett Barkley, David Fridovich-Keil |
| 2025 | Stealix: Model Stealing via Prompt Evolution. Zhixiong Zhuang, Hui-Po Wang, Maria-Irina Nicolae, Mario Fritz |
| 2025 | StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models. Ya Jiang, Chuxiong Wu, Massieh Kordi Boroujeny, Brian L. Mark, Kai Zeng |
| 2025 | Steer LLM Latents for Hallucination Detection. Seongheon Park, Xuefeng Du, Min-Hsuan Yeh, Haobo Wang, Yixuan Li |
| 2025 | Steerable Transformers for Volumetric Data. Soumyabrata Kundu, Risi Kondor |
| 2025 | Steering Protein Language Models. Long-Kai Huang, Rongyi Zhu, Bing He, Jianhua Yao |
| 2025 | Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design. Marcel Hedman, Desi R. Ivanova, Cong Guan, Tom Rainforth |
| 2025 | Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence. Yinbin Han, Meisam Razaviyayn, Renyuan Xu |
| 2025 | Stochastic Deep Restoration Priors for Imaging Inverse Problems. Yuyang Hu, Albert Peng, Weijie Gan, Peyman Milanfar, Mauricio Delbracio, Ulugbek S. Kamilov |
| 2025 | Stochastic Encodings for Active Feature Acquisition. Alexander Luke Ian Norcliffe, Changhee Lee, Fergus Imrie, Mihaela van der Schaar, Pietro Lio |
| 2025 | Stochastic Forward-Backward Deconvolution: Training Diffusion Models with Finite Noisy Datasets. Haoye Lu, Qifan Wu, Yaoliang Yu |
| 2025 | Stochastic Layer-Wise Shuffle for Improving Vision Mamba Training. Zizheng Huang, Haoxing Chen, Jiaqi Li, Jun Lan, Huijia Zhu, Weiqiang Wang, Limin Wang |
| 2025 | Stochastic Online Conformal Prediction with Semi-Bandit Feedback. Haosen Ge, Hamsa Bastani, Osbert Bastani |
| 2025 | Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes. Sidhanth Holalkere, David Bindel, Silvia Sellán, Alexander Terenin |
| 2025 | Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints. Ruichuan Huang, Jiawei Zhang, Ahmet Alacaoglu |
| 2025 | Strategic A/B testing via Maximum Probability-driven Two-armed Bandit. Yu Zhang, Shanshan Zhao, Bokui Wan, Jinjuan Wang, Xiaodong Yan |
| 2025 | Strategic Planning: A Top-Down Approach to Option Generation. Max Ruiz Luyten, Antonin Berthon, Mihaela van der Schaar |
| 2025 | Strategy Coopetition Explains the Emergence and Transience of In-Context Learning. Aaditya K. Singh, Ted Moskovitz, Sara Dragutinovic, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe |
| 2025 | Stray Intrusive Outliers-Based Feature Selection on Intra-Class Asymmetric Instance Distribution or Multiple High-Density Clusters. Lixin Yuan, Yirui Wu, Wenxiao Zhang, Minglei Yuan, Jun Liu |
| 2025 | Stream-level Flow Matching with Gaussian Processes. Ganchao Wei, Li Ma |
| 2025 | Streamline Without Sacrifice - Squeeze out Computation Redundancy in LMM. Penghao Wu, Lewei Lu, Ziwei Liu |
| 2025 | Strengthen Out-of-Distribution Detection Capability with Progressive Self-Knowledge Distillation. Yang Yang, Haonan Xu |
| 2025 | Strong and Weak Identifiability of Optimization-based Causal Discovery in Non-linear Additive Noise Models. Mingjia Li, Hong Qian, Tian-Zuo Wang, Shujun Li, Min Zhang, Aimin Zhou |
| 2025 | Stronger Neyman Regret Guarantees for Adaptive Experimental Design. Georgy Noarov, Riccardo Fogliato, Martín Bertrán, Aaron Roth |
| 2025 | Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs. Jaejun Lee, Joyce Jiyoung Whang |
| 2025 | Structure-Guided Large Language Models for Text-to-SQL Generation. Qinggang Zhang, Hao Chen, Junnan Dong, Shengyuan Chen, Feiran Huang, Xiao Huang |
| 2025 | Structure-informed Risk Minimization for Robust Ensemble Learning. Fengchun Qiao, Yanlin Chen, Xi Peng |
| 2025 | Structured Preconditioners in Adaptive Optimization: A Unified Analysis. Shuo Xie, Tianhao Wang, Sashank J. Reddi, Sanjiv Kumar, Zhiyuan Li |
| 2025 | Sub-Sequential Physics-Informed Learning with State Space Model. Chenhui Xu, Dancheng Liu, Yuting Hu, Jiajie Li, Ruiyang Qin, Qingxiao Zheng, Jinjun Xiong |
| 2025 | Subgoal-Guided Policy Heuristic Search with Learned Subgoals. Jake Tuero, Michael Buro, Levi Lelis |
| 2025 | Subgroups Matter for Robust Bias Mitigation. Anissa Alloula, Charles Jones, Ben Glocker, Bartlomiej W. Papiez |
| 2025 | Subobject-level Image Tokenization. Delong Chen, Samuel Cahyawijaya, Jianfeng Liu, Baoyuan Wang, Pascale Fung |
| 2025 | Subspace Optimization for Large Language Models with Convergence Guarantees. Yutong He, Pengrui Li, Yipeng Hu, Chuyan Chen, Kun Yuan |
| 2025 | Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings. Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot |
| 2025 | Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups. Weiqiu You, Helen Qu, Marco Gatti, Bhuvnesh Jain, Eric Wong |
| 2025 | Sundial: A Family of Highly Capable Time Series Foundation Models. Yong Liu, Guo Qin, Zhiyuan Shi, Zhi Chen, Caiyin Yang, Xiangdong Huang, Jianmin Wang, Mingsheng Long |
| 2025 | Super Deep Contrastive Information Bottleneck for Multi-modal Clustering. Zhengzheng Lou, Ke Zhang, Yucong Wu, Shizhe Hu |
| 2025 | Supercharging Graph Transformers with Advective Diffusion. Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Michael M. Bronstein |
| 2025 | Supervised Contrastive Learning from Weakly-Labeled Audio Segments for Musical Version Matching. Joan Serrà, Recep Oguz Araz, Dmitry Bogdanov, Yuki Mitsufuji |
| 2025 | Surrogate Prompt Learning: Towards Efficient and Diverse Prompt Learning for Vision-Language Models. Liangchen Liu, Nannan Wang, Xi Yang, Xinbo Gao, Tongliang Liu |
| 2025 | Survival Analysis via Density Estimation. Hiroki Yanagisawa, Shunta Akiyama |
| 2025 | Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales. Ju-Seung Byun, Andrew Perrault |
| 2025 | Symmetry-Aware GFlowNets. Hohyun Kim, Seunggeun Lee, Min-hwan Oh |
| 2025 | Symmetry-Driven Discovery of Dynamical Variables in Molecular Simulations. Jeet Mohapatra, Nima Dehmamy, Csaba Both, Subhro Das, Tommi Jaakkola |
| 2025 | Symmetry-Robust 3D Orientation Estimation. Christopher Scarvelis, David Ben-Haim, Paul Zhang |
| 2025 | SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation. Jiayue Liu, Zhongchao Yi, Zhengyang Zhou, Qihe Huang, Kuo Yang, Xu Wang, Yang Wang |
| 2025 | SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering. Xuehang Guo, Xingyao Wang, Yangyi Chen, Sha Li, Chi Han, Manling Li, Heng Ji |
| 2025 | Synonymous Variational Inference for Perceptual Image Compression. Zijian Liang, Kai Niu, Changshuo Wang, Jin Xu, Ping Zhang |
| 2025 | Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability. Chen Wei, Chi Zhang, Jiachen Zou, Haotian Deng, Dietmar Heinke, Quanying Liu |
| 2025 | Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs. Bowen Tan, Zheng Xu, Eric P. Xing, Zhiting Hu, Shanshan Wu |
| 2025 | Synthesizing Software Engineering Data in a Test-Driven Manner. Lei Zhang, Jiaxi Yang, Min Yang, Jian Yang, Mouxiang Chen, Jiajun Zhang, Zeyu Cui, Binyuan Hui, Junyang Lin |
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| 2025 | Synthetic Text Generation for Training Large Language Models via Gradient Matching. Dang Nguyen, Zeman Li, MohammadHossein Bateni, Vahab Mirrokni, Meisam Razaviyayn, Baharan Mirzasoleiman |
| 2025 | System-Aware Unlearning Algorithms: Use Lesser, Forget Faster. Linda Lu, Ayush Sekhari, Karthik Sridharan |
| 2025 | T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling. Zhenyu Hou, Xin Lv, Rui Lu, Jiajie Zhang, Yujiang Li, Zijun Yao, Juanzi Li, Jie Tang, Yuxiao Dong |
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| 2025 | TAROT: Targeted Data Selection via Optimal Transport. Lan Feng, Fan Nie, Yuejiang Liu, Alexandre Alahi |
| 2025 | TCP-Diffusion: A Multi-modal Diffusion Model for Global Tropical Cyclone Precipitation Forecasting with Change Awareness. Cheng Huang, Pan Mu, Cong Bai, Peter AG Watson |
| 2025 | TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization. Mingkang Zhu, Xi Chen, Zhongdao Wang, Bei Yu, Hengshuang Zhao, Jiaya Jia |
| 2025 | TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation. Hyeongwon Jang, Changhun Kim, Eunho Yang |
| 2025 | TINED: GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation. Ziang Zhou, Zhihao Ding, Jieming Shi, Qing Li, Shiqi Shen |
| 2025 | TLLC: Transfer Learning-based Label Completion for Crowdsourcing. Wenjun Zhang, Liangxiao Jiang, Chaoqun Li |
| 2025 | TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction. Hao Li, Hao Wan, Yuzhou Chen, Dongsheng Ye, Yulia Gel, Hao Jiang |
| 2025 | TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference. Jack Min Ong, Matthew Di Ferrante, Aaron Pazdera, Ryan Garner, Sami Jaghouar, Manveer Basra, Max Ryabinin, Johannes Hagemann |
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| 2025 | TRUST-VLM: Thorough Red-Teaming for Uncovering Safety Threats in Vision-Language Models. Kangjie Chen, Muyang Li, Guanlin Li, Shudong Zhang, Shangwei Guo, Tianwei Zhang |
| 2025 | TS-SNN: Temporal Shift Module for Spiking Neural Networks. Kairong Yu, Tianqing Zhang, Qi Xu, Gang Pan, Hongwei Wang |
| 2025 | TSP: A Two-Sided Smoothed Primal-Dual Method for Nonconvex Bilevel Optimization. Songtao Lu |
| 2025 | TTFSFormer: A TTFS-based Lossless Conversion of Spiking Transformer. Lusen Zhao, Zihan Huang, Jianhao Ding, Zhaofei Yu |
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| 2025 | TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments. Zi-Jian Cheng, Ziyi Jia, Zhi Zhou, Yufeng Li, Lan-Zhe Guo |
| 2025 | TabFlex: Scaling Tabular Learning to Millions with Linear Attention. Yuchen Zeng, Tuan Dinh, Wonjun Kang, Andreas C. Mueller |
| 2025 | TabICL: A Tabular Foundation Model for In-Context Learning on Large Data. Jingang Qu, David Holzmüller, Gaël Varoquaux, Marine Le Morvan |
| 2025 | TabNAT: A Continuous-Discrete Joint Generative Framework for Tabular Data. Hengrui Zhang, Liancheng Fang, Qitian Wu, Philip S. Yu |
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| 2025 | Tackling View-Dependent Semantics in 3D Language Gaussian Splatting. Jiazhong Cen, Xudong Zhou, Jiemin Fang, Changsong Wen, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian |
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| 2025 | Targeted Unlearning with Single Layer Unlearning Gradient. Zikui Cai, Yaoteng Tan, M. Salman Asif |
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| 2025 | Teaching Physical Awareness to LLMs through Sounds. Weiguo Wang, Andy Nie, Wenrui Zhou, Yi Kai, Chengchen Hu |
| 2025 | Teaching Transformers Causal Reasoning through Axiomatic Training. Aniket Vashishtha, Abhinav Kumar, Atharva Pandey, Abbavaram Gowtham Reddy, Kabir Ahuja, Vineeth N. Balasubramanian, Amit Sharma |
| 2025 | Telling Peer Direct Effects from Indirect Effects in Observational Network Data. Xiaojing Du, Jiuyong Li, Debo Cheng, Lin Liu, Wentao Gao, Xiongren Chen, Ziqi Xu |
| 2025 | Temperature-Annealed Boltzmann Generators. Henrik Schopmans, Pascal Friederich |
| 2025 | Temporal Difference Flows. Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Rémi Munos, Alessandro Lazaric, Ahmed Touati |
| 2025 | Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning. Dongsu Lee, Minhae Kwon |
| 2025 | Temporal Misalignment in ANN-SNN Conversion and its Mitigation via Probabilistic Spiking Neurons. Velibor Bojkovic, Xiaofeng Wu, Bin Gu |
| 2025 | Temporal Query Network for Efficient Multivariate Time Series Forecasting. Shengsheng Lin, Haojun Chen, Haijie Wu, Chunyun Qiu, Weiwei Lin |
| 2025 | Tensor Decomposition Based Memory-Efficient Incremental Learning. Yuhang Li, Guoxu Zhou, Zhenhao Huang, Xinqi Chen, Yuning Qiu, Qibin Zhao |
| 2025 | Tensor Product Neural Networks for Functional ANOVA Model. Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, Yongdai Kim |
| 2025 | Tensor-Var: Efficient Four-Dimensional Variational Data Assimilation. Yiming Yang, Xiaoyuan Cheng, Daniel Giles, Sibo Cheng, Yi He, Xiao Xue, Boli Chen, Yukun Hu |
| 2025 | Tensorized Multi-View Multi-Label Classification via Laplace Tensor Rank. Qiyu Zhong, Yi Shan, Haobo Wang, Zhen Yang, Gengyu Lyu |
| 2025 | Test-Time Adaptation for Online Vision-Language Navigation with Feedback-based Reinforcement Learning. Sungjune Kim, Gyeongrok Oh, Heeju Ko, Daehyun Ji, Dongwook Lee, Byung-Jun Lee, Sujin Jang, Sangpil Kim |
| 2025 | Test-Time Adaptation with Binary Feedback. Taeckyung Lee, Sorn Chottananurak, Junsu Kim, Jinwoo Shin, Taesik Gong, Sung-Ju Lee |
| 2025 | Test-Time Canonicalization by Foundation Models for Robust Perception. Utkarsh Singhal, Ryan Feng, Stella X. Yu, Atul Prakash |
| 2025 | Test-Time Graph Neural Dataset Search With Generative Projection. Xin Zheng, Wei Huang, Chuan Zhou, Ming Li, Shirui Pan |
| 2025 | Test-Time Learning for Large Language Models. Jinwu Hu, Zitian Zhang, Guohao Chen, Xutao Wen, Chao Shuai, Wei Luo, Bin Xiao, Yuanqing Li, Mingkui Tan |
| 2025 | Test-Time Multimodal Backdoor Detection by Contrastive Prompting. Yuwei Niu, Shuo He, Qi Wei, Zongyu Wu, Feng Liu, Lei Feng |
| 2025 | Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback. Yafu Li, Xuyang Hu, Xiaoye Qu, Linjie Li, Yu Cheng |
| 2025 | Test-Time Selective Adaptation for Uni-Modal Distribution Shift in Multi-Modal Data. Mingcai Chen, Baoming Zhang, Zongbo Han, Wenyu Jiang, Yanmeng Wang, Shuai Feng, Yuntao Du, Bingkun Bao |
| 2025 | Test-Time Training Provably Improves Transformers as In-context Learners. Halil Alperen Gozeten, Muhammed Emrullah Ildiz, Xuechen Zhang, Mahdi Soltanolkotabi, Marco Mondelli, Samet Oymak |
| 2025 | Test-time Adaptation on Graphs via Adaptive Subgraph-based Selection and Regularized Prototypes. Yusheng Zhao, Qixin Zhang, Xiao Luo, Junyu Luo, Wei Ju, Zhiping Xiao, Ming Zhang |
| 2025 | Test-time Adapted Reinforcement Learning with Action Entropy Regularization. Shoukai Xu, Zihao Lian, Mingkui Tan, Liu Liu, Zhong Zhang, Peilin Zhao |
| 2025 | Test-time Correlation Alignment. Linjing You, Jiabao Lu, Xiayuan Huang |
| 2025 | Testing Conditional Mean Independence Using Generative Neural Networks. Yi Zhang, Linjun Huang, Yun Yang, Xiaofeng Shao |
| 2025 | Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models. Luca M. Schulze Buschoff, Konstantinos Voudouris, Elif Akata, Matthias Bethge, Joshua B. Tenenbaum, Eric Schulz |
| 2025 | Text-to-CAD Generation Through Infusing Visual Feedback in Large Language Models. Ruiyu Wang, Yu Yuan, Shizhao Sun, Jiang Bian |
| 2025 | Text-to-LoRA: Instant Transformer Adaption. Rujikorn Charakorn, Edoardo Cetin, Yujin Tang, Robert Tjarko Lange |
| 2025 | TextCenGen: Attention-Guided Text-Centric Background Adaptation for Text-to-Image Generation. Tianyi Liang, Jiangqi Liu, Yifei Huang, Shiqi Jiang, Jianshen Shi, Changbo Wang, Chenhui Li |
| 2025 | Textual Unlearning Gives a False Sense of Unlearning. Jiacheng Du, Zhibo Wang, Jie Zhang, Xiaoyi Pang, Jiahui Hu, Kui Ren |
| 2025 | Textural or Textual: How Vision-Language Models Read Text in Images. Hanzhang Wang, Qingyuan Ma |
| 2025 | The Batch Complexity of Bandit Pure Exploration. Adrienne Tuynman, Rémy Degenne |
| 2025 | The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models. Shishir G. Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, Joseph E. Gonzalez |
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| 2025 | The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning. Dulhan Jayalath, Gilad Landau, Brendan Shillingford, Mark W. Woolrich, Oiwi Parker Jones |
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| 2025 | The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text. Matthieu Meeus, Lukas Wutschitz, Santiago Zanella-Béguelin, Shruti Tople, Reza Shokri |
| 2025 | The Case for Learned Provenance-based System Behavior Baseline. Yao Zhu, Zhenyuan Li, Yangyang Wei, Shouling Ji |
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| 2025 | The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning. Jiashun Liu, Johan S. Obando-Ceron, Pablo Samuel Castro, Aaron C. Courville, Ling Pan |
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| 2025 | The Diffusion Duality. Subham Sekhar Sahoo, Justin Deschenaux, Aaron Gokaslan, Guanghan Wang, Justin T. Chiu, Volodymyr Kuleshov |
| 2025 | The Disparate Benefits of Deep Ensembles. Kajetan Schweighofer, Adrián Arnaiz-Rodríguez, Sepp Hochreiter, Nuria Oliver |
| 2025 | The Double-Ellipsoid Geometry of CLIP. Meir Yossef Levi, Guy Gilboa |
| 2025 | The Elicitation Game: Evaluating Capability Elicitation Techniques. Felix Hofstätter, Teun van der Weij, Jayden Teoh, Rada Djoneva, Henning Bartsch, Francis Rhys Ward |
| 2025 | The Emperor's New Clothes in Benchmarking? A Rigorous Examination of Mitigation Strategies for LLM Benchmark Data Contamination. Yifan Sun, Han Wang, Dongbai Li, Gang Wang, Huan Zhang |
| 2025 | The Empirical Mean is Minimax Optimal for Local Glivenko-Cantelli. Doron Cohen, Aryeh Kontorovich, Roi Weiss |
| 2025 | The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking. Yuchun Miao, Sen Zhang, Liang Ding, Yuqi Zhang, Lefei Zhang, Dacheng Tao |
| 2025 | The Four Color Theorem for Cell Instance Segmentation. Ye Zhang, Yu Zhou, Yifeng Wang, Jun Xiao, Ziyue Wang, Yongbing Zhang, Jianxu Chen |
| 2025 | The Generalized Skew Spectrum of Graphs. Armando Bellante, Martin Plávala, Alessandro Luongo |
| 2025 | The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence. Tom Wollschläger, Jannes Elstner, Simon Geisler, Vincent Cohen-Addad, Stephan Günnemann, Johannes Gasteiger |
| 2025 | The Global Convergence Time of Stochastic Gradient Descent in Non-Convex Landscapes: Sharp Estimates via Large Deviations. Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos |
| 2025 | The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback. Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Michal Valko, Vianney Perchet |
| 2025 | The Hidden Dimensions of LLM Alignment: A Multi-Dimensional Analysis of Orthogonal Safety Directions. Wenbo Pan, Zhichao Liu, Qiguang Chen, Xiangyang Zhou, Haining Yu, Xiaohua Jia |
| 2025 | The Hidden Joules: Evaluating the Energy Consumption of Vision Backbones for Progress Towards More Efficient Model Inference. Zeyu Yang, Wesley Armour |
| 2025 | The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models Via Visual Information Steering. Zhuowei Li, Haizhou Shi, Yunhe Gao, Di Liu, Zhenting Wang, Yuxiao Chen, Ting Liu, Long Zhao, Hao Wang, Dimitris N. Metaxas |
| 2025 | The Illusion of Role Separation: Hidden Shortcuts in LLM Role Learning (and How to Fix Them). Zihao Wang, Yibo Jiang, Jiahao Yu, Heqing Huang |
| 2025 | The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks. Walter Mayor, Johan S. Obando-Ceron, Aaron C. Courville, Pablo Samuel Castro |
| 2025 | The Importance of Being Lazy: Scaling Limits of Continual Learning. Jacopo Graldi, Alessandro Breccia, Giulia Lanzillotta, Thomas Hofmann, Lorenzo Noci |
| 2025 | The Jailbreak Tax: How Useful are Your Jailbreak Outputs? Kristina Nikolic, Luze Sun, Jie Zhang, Florian Tramèr |
| 2025 | The Limits of Predicting Agents from Behaviour. Alexis Bellot, Jonathan Richens, Tom Everitt |
| 2025 | The Limits of Tractable Marginalization. Oliver Broadrick, Sanyam Agarwal, Guy Van den Broeck, Markus Bläser |
| 2025 | The Lock-in Hypothesis: Stagnation by Algorithm. Tianyi Qiu, Zhonghao He, Tejasveer Chugh, Max Kleiman-Weiner |
| 2025 | The Logical Implication Steering Method for Conditional Interventions on Transformer Generation. Damjan Kalajdzievski |
| 2025 | The Missing Alignment Link of In-context Learning on Sequences. Harshvardhan Agarwal, Sunita Sarawagi |
| 2025 | The Noisy Laplacian: a Threshold Phenomenon for Non-Linear Dimension Reduction. Alex Kokot, Octavian-Vlad Murad, Marina Meila |
| 2025 | The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes. Pedro P. Santos, Alberto Sardinha, Francisco S. Melo |
| 2025 | The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning. Shiwei Li, Xiandi Luo, Haozhao Wang, Xing Tang, Shijie Xu, Weihong Luo, Yuhua Li, Xiuqiang He, Ruixuan Li |
| 2025 | The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret. Lukas Fluri, Leon Lang, Alessandro Abate, Patrick Forré, David Krueger, Joar Max Viktor Skalse |
| 2025 | The Polynomial Stein Discrepancy for Assessing Moment Convergence. Narayan Srinivasan, Matthew Sutton, Christopher C. Drovandi, Leah F. South |
| 2025 | The Power of Random Features and the Limits of Distribution-Free Gradient Descent. Ari Karchmer, Eran Malach |
| 2025 | The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products. Yuqing Xie, Ameya Daigavane, Mit Kotak, Tess E. Smidt |
| 2025 | The Price of Linear Time: Error Analysis of Structured Kernel Interpolation. Alexander Moreno, Justin Xiao, Jonathan Mei |
| 2025 | The Relationship Between No-Regret Learning and Online Conformal Prediction. Ramya Ramalingam, Shayan Kiyani, Aaron Roth |
| 2025 | The Ripple Effect: On Unforeseen Complications of Backdoor Attacks. Rui Zhang, Yun Shen, Hongwei Li, Wenbo Jiang, Hanxiao Chen, Yuan Zhang, Guowen Xu, Yang Zhang |
| 2025 | The Role of Randomness in Stability. Max Hopkins, Shay Moran |
| 2025 | The Role of Sparsity for Length Generalization in LLMs. Noah Golowich, Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach |
| 2025 | The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability. Jiachen Hu, Rui Ai, Han Zhong, Xiaoyu Chen, Liwei Wang, Zhaoran Wang, Zhuoran Yang |
| 2025 | The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training. Jinbo Wang, Mingze Wang, Zhanpeng Zhou, Junchi Yan, Weinan E, Lei Wu |
| 2025 | The Sparse-Plus-Low-Rank Quasi-Newton Method for Entropic-Regularized Optimal Transport. Chenrui Wang, Yixuan Qiu |
| 2025 | The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training. Fabian Schaipp, Alexander Hägele, Adrien B. Taylor, Umut Simsekli, Francis Bach |
| 2025 | The Surprising Effectiveness of Test-Time Training for Few-Shot Learning. Ekin Akyürek, Mehul Damani, Adam Zweiger, Linlu Qiu, Han Guo, Jyothish Pari, Yoon Kim, Jacob Andreas |
| 2025 | The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data. Thomas Pouplin, Kasia Kobalczyk, Hao Sun, Mihaela van der Schaar |
| 2025 | The Underlying Universal Statistical Structure of Natural Datasets. Noam Itzhak Levi, Yaron Oz |
| 2025 | The Value of Prediction in Identifying the Worst-Off. Unai Fischer Abaigar, Christoph Kern, Juan Carlos Perdomo |
| 2025 | The dark side of the forces: assessing non-conservative force models for atomistic machine learning. Filippo Bigi, Marcel F. Langer, Michele Ceriotti |
| 2025 | The impact of uncertainty on regularized learning in games. Pierre-Louis Cauvin, Davide Legacci, Panayotis Mertikopoulos |
| 2025 | The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training. Matteo Saponati, Pascal Sager, Pau Vilimelis Aceituno, Thilo Stadelmann, Benjamin F. Grewe |
| 2025 | Theoretical Limitations of Ensembles in the Age of Overparameterization. Niclas Dern, John Patrick Cunningham, Geoff Pleiss |
| 2025 | Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport. Jayadev Naram, Fredrik Hellström, Ziming Wang, Rebecka Jörnsten, Giuseppe Durisi |
| 2025 | Theoretical guarantees on the best-of-n alignment policy. Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander Nicholas D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh |
| 2025 | Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models. Quan Minh Nguyen, Minh N. Vu, Truc Nguyen, My T. Thai |
| 2025 | Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos. Christian Pedersen, Laure Zanna, Joan Bruna |
| 2025 | Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks. Sungwon Kim, Namkyeong Lee, Yunyoung Doh, Seungmin Shin, Guimok Cho, Seung-Won Jeon, Sangkook Kim, Chanyoung Park |
| 2025 | Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization. Zishun Yu, Tengyu Xu, Di Jin, Karthik Abinav Sankararaman, Yun He, Wenxuan Zhou, Zhouhao Zeng, Eryk Helenowski, Chen Zhu, Sinong Wang, Hao Ma, Han Fang |
| 2025 | Think Twice, Act Once: A Co-Evolution Framework of LLM and RL for Large-Scale Decision Making. Xu Wan, Wenyue Xu, Chao Yang, Mingyang Sun |
| 2025 | Thinking LLMs: General Instruction Following with Thought Generation. Tianhao Wu, Janice Lan, Weizhe Yuan, Jiantao Jiao, Jason E. Weston, Sainbayar Sukhbaatar |
| 2025 | Three-Dimensional Trajectory Prediction with 3DMoTraj Dataset. Hao Zhou, Xu Yang, Mingyu Fan, Lu Qi, Xiangtai Li, Ming-Hsuan Yang, Fei Luo |
| 2025 | Tight and Fast Bounds for Multi-Label Learning. Yifan Zhang, Min-Ling Zhang |
| 2025 | Tightening Causal Bounds via Covariate-Aware Optimal Transport. Sirui Lin, Zijun Gao, Jose H. Blanchet, Peter W. Glynn |
| 2025 | Tilted Sharpness-Aware Minimization. Tian Li, Tianyi Zhou, Jeff A. Bilmes |
| 2025 | Time Series Representations with Hard-Coded Invariances. Thibaut Germain, Chrysoula Kosma, Laurent Oudre |
| 2025 | Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time. Duc Anh Nguyen, Ernesto Araya, Adalbert Fono, Gitta Kutyniok |
| 2025 | Time-Aware World Model for Adaptive Prediction and Control. Anh N. Nhu, Sanghyun Son, Ming Lin |
| 2025 | Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting. Siru Zhong, Weilin Ruan, Ming Jin, Huan Li, Qingsong Wen, Yuxuan Liang |
| 2025 | TimeBase: The Power of Minimalism in Efficient Long-term Time Series Forecasting. Qihe Huang, Zhengyang Zhou, Kuo Yang, Zhongchao Yi, Xu Wang, Yang Wang |
| 2025 | TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting. Peiyuan Liu, Beiliang Wu, Yifan Hu, Naiqi Li, Tao Dai, Jigang Bao, Shu-Tao Xia |
| 2025 | TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation. Daoyu Wang, Mingyue Cheng, Zhiding Liu, Qi Liu |
| 2025 | TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting. Yifan Hu, Guibin Zhang, Peiyuan Liu, Disen Lan, Naiqi Li, Dawei Cheng, Tao Dai, Shu-Tao Xia, Shirui Pan |
| 2025 | TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning. Ron Shapira Weber, Shahar Ben Ishay, Andrey Lavrinenko, Shahaf E. Finder, Oren Freifeld |
| 2025 | TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state. Xiaowen Ma, Zhen-Liang Ni, Shuai Xiao, Xinghao Chen |
| 2025 | TimeStacker: A Novel Framework with Multilevel Observation for Capturing Nonstationary Patterns in Time Series Forecasting. Qinglong Liu, Cong Xu, Wenhao Jiang, Kaixuan Wang, Lin Ma, Haifeng Li |
| 2025 | TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision. Shaobin Zhuang, Yiwei Guo, Yanbo Ding, Kunchang Li, Xinyuan Chen, Yaohui Wang, Fangyikang Wang, Ying Zhang, Chen Li, Yali Wang |
| 2025 | TinyMIG: Transferring Generalization from Vision Foundation Models to Single-Domain Medical Imaging. Chuang Liu, Hongyan Xu, Yichao Cao, Xiu Su, Zhe Qu, Tianfa Li, Shan An, Haogang Zhu |
| 2025 | To Each Metric Its Decoding: Post-Hoc Optimal Decision Rules of Probabilistic Hierarchical Classifiers. Roman Plaud, Alexandre Perez-Lebel, Matthieu Labeau, Antoine Saillenfest, Thomas Bonald |
| 2025 | To Steer or Not to Steer? Mechanistic Error Reduction with Abstention for Language Models. Anna Hedström, Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Manuela Veloso |
| 2025 | ToMA: Token Merge with Attention for Diffusion Models. Wenbo Lu, Shaoyi Zheng, Yuxuan Xia, Shengjie Wang |
| 2025 | Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning. DiJia Su, Hanlin Zhu, Yingchen Xu, Jiantao Jiao, Yuandong Tian, Qinqing Zheng |
| 2025 | Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning. Jinlong Pang, Na Di, Zhaowei Zhu, Jiaheng Wei, Hao Cheng, Chen Qian, Yang Liu |
| 2025 | Token Coordinated Prompt Attention is Needed for Visual Prompting. Zichen Liu, Xu Zou, Gang Hua, Jiahuan Zhou |
| 2025 | Token Signature: Predicting Chain-of-Thought Gains with Token Decoding Feature in Large Language Models. Peijie Liu, Fengli Xu, Yong Li |
| 2025 | TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation. Tong Wu, Junzhe Shen, Zixia Jia, Yuxuan Wang, Zilong Zheng |
| 2025 | Tokenized Bandit for LLM Decoding and Alignment. Suho Shin, Chenghao Yang, Haifeng Xu, MohammadTaghi Hajiaghayi |
| 2025 | Tool Unlearning for Tool-Augmented LLMs. Jiali Cheng, Hadi Amiri |
| 2025 | TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration. Cheng Xin, Fan Xu, Xin Ding, Jie Gao, Jiaxin Ding |
| 2025 | TopoTune: A Framework for Generalized Combinatorial Complex Neural Networks. Mathilde Papillon, Guillermo Bernárdez, Claudio Battiloro, Nina Miolane |
| 2025 | Topological Signatures of Adversaries in Multimodal Alignments. Minh Nhat Vu, Geigh Zollicoffer, Huy Quang Mai, Ben Nebgen, Boian S. Alexandrov, Manish Bhattarai |
| 2025 | Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph. Weihuang Zheng, Jiashuo Liu, Jiaxing Li, Jiayun Wu, Peng Cui, Youyong Kong |
| 2025 | Topology-aware Neural Flux Prediction Guided by Physics. Haoyang Jiang, Jindong Wang, Xingquan Zhu, Yi He |
| 2025 | Toward Data-centric Directed Graph Learning: An Entropy-driven Approach. Xunkai Li, Zhengyu Wu, Kaichi Yu, Hongchao Qin, Guang Zeng, Rong-Hua Li, Guoren Wang |
| 2025 | Toward Efficient Kernel-Based Solvers for Nonlinear PDEs. Zhitong Xu, Da Long, Yiming Xu, Guang Yang, Shandian Zhe, Houman Owhadi |
| 2025 | Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage. Saehyung Lee, Seunghyun Yoon, Trung Bui, Jing Shi, Sungroh Yoon |
| 2025 | Toward a Unified Theory of Gradient Descent under Generalized Smoothness. Alexander Tyurin |
| 2025 | Towards Attributions of Input Variables in a Coalition. Xinhao Zheng, Huiqi Deng, Quanshi Zhang |
| 2025 | Towards Better-than-2 Approximation for Constrained Correlation Clustering. Andreas Kalavas, Evangelos Kipouridis, Nithin Varma |
| 2025 | Towards Black-Box Membership Inference Attack for Diffusion Models. Jingwei Li, Jing Dong, Tianxing He, Jingzhao Zhang |
| 2025 | Towards Cost-Effective Reward Guided Text Generation. Ahmad Rashid, Ruotian Wu, Rongqi Fan, Hongliang Li, Agustinus Kristiadi, Pascal Poupart |
| 2025 | Towards Efficient Online Tuning of VLM Agents via Counterfactual Soft Reinforcement Learning. Lang Feng, Weihao Tan, Zhiyi Lyu, Longtao Zheng, Haiyang Xu, Ming Yan, Fei Huang, Bo An |
| 2025 | Towards Escaping from Class Dependency Modeling for Multi-Dimensional Classification. Teng Huang, Bin-Bin Jia, Min-Ling Zhang |
| 2025 | Towards Global-level Mechanistic Interpretability: A Perspective of Modular Circuits of Large Language Models. Yinhan He, Wendy Zheng, Yushun Dong, Yaochen Zhu, Chen Chen, Jundong Li |
| 2025 | Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees. Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye |
| 2025 | Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond. Chongyu Fan, Jinghan Jia, Yihua Zhang, Anil Ramakrishna, Mingyi Hong, Sijia Liu |
| 2025 | Towards Learning to Complete Anything in Lidar. Ayça Takmaz, Cristiano Saltori, Neehar Peri, Tim Meinhardt, Riccardo de Lutio, Laura Leal-Taixé, Aljosa Osep |
| 2025 | Towards Lifelong Model Editing via Simulating Ideal Editor. Yaming Guo, Siyang Guo, Hengshu Zhu, Ying Sun |
| 2025 | Towards Memorization Estimation: Fast, Formal and Free. Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi, Kaushik Roy |
| 2025 | Towards Practical Defect-Focused Automated Code Review. Junyi Lu, Lili Jiang, Xiaojia Li, Jianbing Fang, Fengjun Zhang, Li Yang, Chun Zuo |
| 2025 | Towards Rationale-Answer Alignment of LVLMs via Self-Rationale Calibration. Yuanchen Wu, Ke Yan, Shouhong Ding, Ziyin Zhou, Xiaoqiang Li |
| 2025 | Towards Robust Influence Functions with Flat Validation Minima. Xichen Ye, Yifan Wu, Weizhong Zhang, Cheng Jin, Yifan Chen |
| 2025 | Towards Robustness and Explainability of Automatic Algorithm Selection. Xingyu Wu, Jibin Wu, Yu Zhou, Liang Feng, Kc Tan |
| 2025 | Towards Theoretical Understanding of Sequential Decision Making with Preference Feedback. Simone Drago, Marco Mussi, Alberto Maria Metelli |
| 2025 | Towards Trustworthy Federated Learning with Untrusted Participants. Youssef Allouah, Rachid Guerraoui, John Stephan |
| 2025 | Towards Understanding Catastrophic Forgetting in Two-layer Convolutional Neural Networks. Boqi Li, Youjun Wang, Weiwei Liu |
| 2025 | Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis. Xu Wang, Yan Hu, Wenyu Du, Reynold Cheng, Benyou Wang, Difan Zou |
| 2025 | Towards Understanding Gradient Dynamics of the Sliced-Wasserstein Distance via Critical Point Analysis. Christophe Vauthier, Anna Korba, Quentin Mérigot |
| 2025 | Towards Understanding Parametric Generalized Category Discovery on Graphs. Bowen Deng, Lele Fu, Jialong Chen, Sheng Huang, Tianchi Liao, Zhang Tao, Chuan Chen |
| 2025 | Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings. Rong-Xi Tan, Ming Chen, Ke Xue, Yao Wang, Yaoyuan Wang, Sheng Fu, Chao Qian |
| 2025 | Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation. Fanqing Meng, Jiaqi Liao, Xinyu Tan, Quanfeng Lu, Wenqi Shao, Kaipeng Zhang, Yu Cheng, Dianqi Li, Ping Luo |
| 2025 | Towards a Formal Theory of Representational Compositionality. Eric Elmoznino, Thomas Jiralerspong, Yoshua Bengio, Guillaume Lajoie |
| 2025 | Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer. Yihang Wang, Yuying Qiu, Peng Chen, Kai Zhao, Yang Shu, Zhongwen Rao, Lujia Pan, Bin Yang, Chenjuan Guo |
| 2025 | Towards a Mechanistic Explanation of Diffusion Model Generalization. Matthew Niedoba, Berend Zwartsenberg, Kevin Patrick Murphy, Frank Wood |
| 2025 | Towards a Unified Framework of Clustering-based Anomaly Detection. Zeyu Fang, Ming Gu, Sheng Zhou, Jiawei Chen, Qiaoyu Tan, Haishuai Wang, Jiajun Bu |
| 2025 | Towards an Explainable Comparison and Alignment of Feature Embeddings. Mohammad Jalali, Bahar Dibaei Nia, Farzan Farnia |
| 2025 | Towards characterizing the value of edge embeddings in Graph Neural Networks. Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski |
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| 2025 | Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models. Konstantin Donhauser, Kristina Ulicna, Gemma E. Moran, Aditya Ravuri, Kian Kenyon-Dean, Cian Eastwood, Jason S. Hartford |
| 2025 | Towards the Causal Complete Cause of Multi-Modal Representation Learning. Jingyao Wang, Siyu Zhao, Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Fuchun Sun, Hui Xiong |
| 2025 | Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift. Chao Ying, Jun Jin, Yi Guo, Xiudi Li, Muxuan Liang, Jiwei Zhao |
| 2025 | TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction. Shi Yin, Xinyang Pan, Fengyan Wang, Lixin He |
| 2025 | Tracking Most Significant Shifts in Infinite-Armed Bandits. Joe Suk, Jung-Hun Kim |
| 2025 | Tracking The Best Expert Privately. Hilal Asi, Vinod Raman, Aadirupa Saha |
| 2025 | Tractable Transformers for Flexible Conditional Generation. Anji Liu, Xuejie Liu, Dayuan Zhao, Mathias Niepert, Yitao Liang, Guy Van den Broeck |
| 2025 | Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions. Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M. Kakade, Sitan Chen |
| 2025 | Training Deep Learning Models with Norm-Constrained LMOs. Thomas Pethick, Wanyun Xie, Kimon Antonakopoulos, Zhenyu Zhu, Antonio Silveti-Falls, Volkan Cevher |
| 2025 | Training Diffusion-based Generative Models with Limited Data. Zhaoyu Zhang, Yang Hua, Guanxiong Sun, Hui Wang, Seán F. McLoone |
| 2025 | Training Dynamics of In-Context Learning in Linear Attention. Yedi Zhang, Aaditya K. Singh, Peter E. Latham, Andrew M. Saxe |
| 2025 | Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra. Alan Nawzad Amin, Andres Potapczynski, Andrew Gordon Wilson |
| 2025 | Training High Performance Spiking Neural Network by Temporal Model Calibration. Jiaqi Yan, Changping Wang, De Ma, Huajin Tang, Qian Zheng, Gang Pan |
| 2025 | Training Software Engineering Agents and Verifiers with SWE-Gym. Jiayi Pan, Xingyao Wang, Graham Neubig, Navdeep Jaitly, Heng Ji, Alane Suhr, Yizhe Zhang |
| 2025 | Training a Generally Curious Agent. Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J. Zico Kolter, Jeff Schneider, Russ Salakhutdinov |
| 2025 | Trajectory Inference with Smooth Schrödinger Bridges. Wanli Hong, Yuliang Shi, Jonathan Niles-Weed |
| 2025 | Trajectory World Models for Heterogeneous Environments. Shaofeng Yin, Jialong Wu, Siqiao Huang, Xingjian Su, Xu He, Jianye Hao, Mingsheng Long |
| 2025 | TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation. Jaeho Kim, Seulki Lee |
| 2025 | Transfer Learning for Nonparametric Contextual Dynamic Pricing. Fan Wang, Feiyu Jiang, Zifeng Zhao, Yi Yu |
| 2025 | Transfer Q-Learning with Composite MDP Structures. Jinhang Chai, Elynn Y. Chen, Lin Yang |
| 2025 | Transformative or Conservative? Conservation laws for ResNets and Transformers. Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré |
| 2025 | Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation. He Li, Haoang Chi, Mingyu Liu, Wanrong Huang, Liyang Xu, Wenjing Yang |
| 2025 | Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries. Huakun Luo, Haixu Wu, Hang Zhou, Lanxiang Xing, Yichen Di, Jianmin Wang, Mingsheng Long |
| 2025 | Tree-Sliced Wasserstein Distance with Nonlinear Projection. Thanh Tran, Hoang V. Tran, Thanh T. Chu, Huyen Trang Pham, Laurent El Ghaoui, Tam Le, Tan Minh Nguyen |
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| 2025 | TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree. Yu-Yang Qian, Yuan-Ze Xu, Zhen-Yu Zhang, Peng Zhao, Zhi-Hua Zhou |
| 2025 | Triple-Optimistic Learning for Stochastic Contextual Bandits with General Constraints. Hengquan Guo, Lingkai Zu, Xin Liu |
| 2025 | Trust-Region Twisted Policy Improvement. Joery A. de Vries, Jinke He, Yaniv Oren, Matthijs T. J. Spaan |
| 2025 | Trusted Multi-View Classification with Expert Knowledge Constraints. Xinyan Liang, Shijie Wang, Yuhua Qian, Qian Guo, Liang Du, Bingbing Jiang, Tingjin Luo, Feijiang Li |
| 2025 | Trustworthy Machine Learning through Data-Specific Indistinguishability. Hanshen Xiao, Zhen Yang, G. Edward Suh |
| 2025 | TruthFlow: Truthful LLM Generation via Representation Flow Correction. Hanyu Wang, Bochuan Cao, Yuanpu Cao, Jinghui Chen |
| 2025 | TtBA: Two-third Bridge Approach for Decision-Based Adversarial Attack. Feiyang Wang, Xingquan Zuo, Hai Huang, Gang Chen |
| 2025 | TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs. Felipe Pinto Coelho Nuti, Tim Franzmeyer, João F. Henriques |
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| 2025 | UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models. Xin Xu, Qiyun Xu, Tong Xiao, Tianhao Chen, Yuchen Yan, Jiaxin Zhang, Shizhe Diao, Can Yang, Yang Wang |
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| 2025 | UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent. Jianke Zhang, Yanjiang Guo, Yucheng Hu, Xiaoyu Chen, Xiang Zhu, Jianyu Chen |
| 2025 | Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion. Anle Ke, Xu Zhang, Tong Chen, Ming Lu, Chao Zhou, Jiawen Gu, Zhan Ma |
| 2025 | Ultra-Resolution Adaptation with Ease. Ruonan Yu, Songhua Liu, Zhenxiong Tan, Xinchao Wang |
| 2025 | UltraTWD: Optimizing Ultrametric Trees for Tree-Wasserstein Distance. Fangchen Yu, Yanzhen Chen, Jiaxing Wei, Jianfeng Mao, Wenye Li, Qiang Sun |
| 2025 | UnHiPPO: Uncertainty-aware Initialization for State Space Models. Marten Lienen, Abdullah Saydemir, Stephan Günnemann |
| 2025 | Unbiased Evaluation of Large Language Models from a Causal Perspective. Meilin Chen, Jian Tian, Liang Ma, Di Xie, Weijie Chen, Jiang Zhu |
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| 2025 | Uncertainty Quantification for LLM-Based Survey Simulations. Chengpiao Huang, Yuhang Wu, Kaizheng Wang |
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| 2025 | Unconstrained Robust Online Convex Optimization. Jiujia Zhang, Ashok Cutkosky |
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| 2025 | Understanding Complexity in VideoQA via Visual Program Generation. Cristóbal Eyzaguirre, Igor Vasiljevic, Achal Dave, Jiajun Wu, Rares Andrei Ambrus, Thomas Kollar, Juan Carlos Niebles, Pavel Tokmakov |
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| 2025 | Understanding Generalization in Quantum Machine Learning with Margins. Tak Hur, Daniel K. Park |
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| 2025 | Understanding Model Ensemble in Transferable Adversarial Attack. Wei Yao, Zeliang Zhang, Huayi Tang, Yong Liu |
| 2025 | Understanding Model Reprogramming for CLIP via Decoupling Visual Prompts. Chengyi Cai, Zesheng Ye, Lei Feng, Jianzhong Qi, Feng Liu |
| 2025 | Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach. Changdae Oh, Zhen Fang, Shawn Im, Xuefeng Du, Yixuan Li |
| 2025 | Understanding Nonlinear Implicit Bias via Region Counts in Input Space. Jingwei Li, Jing Xu, Zifan Wang, Huishuai Zhang, Jingzhao Zhang |
| 2025 | Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods. Yifan Hao, Xingyuan Pan, Hanning Zhang, Chenlu Ye, Rui Pan, Tong Zhang |
| 2025 | Understanding Sharpness Dynamics in NN Training with a Minimalist Example: The Effects of Dataset Difficulty, Depth, Stochasticity, and More. Geonhui Yoo, Minhak Song, Chulhee Yun |
| 2025 | Understanding Synthetic Context Extension via Retrieval Heads. Xinyu Zhao, Fangcong Yin, Greg Durrett |
| 2025 | Understanding and Improving Length Generalization in Recurrent Models. Ricardo Buitrago Ruiz, Albert Gu |
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| 2025 | Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models. Shuoyuan Wang, Yixuan Li, Hongxin Wei |
| 2025 | Understanding the Emergence of Multimodal Representation Alignment. Megan Tjandrasuwita, Chanakya Ekbote, Liu Ziyin, Paul Pu Liang |
| 2025 | Understanding the Forgetting of (Replay-based) Continual Learning via Feature Learning: Angle Matters. Hongyi Wang, Shiyuan Ren, Wei Huang, Miao Zhang, Xiang Deng, Yixin Bao, Liqiang Nie |
| 2025 | Understanding the Kronecker Matrix-Vector Complexity of Linear Algebra. Raphael A. Meyer, William J. Swartworth, David P. Woodruff |
| 2025 | Understanding the Limits of Deep Tabular Methods with Temporal Shift. Haorun Cai, Han-Jia Ye |
| 2025 | Understanding the Logic of Direct Preference Alignment through Logic. Kyle Richardson, Vivek Srikumar, Ashish Sabharwal |
| 2025 | Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism. Aviv Bick, Eric P. Xing, Albert Gu |
| 2025 | Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees. Xin Yu, Zelin He, Ying Sun, Lingzhou Xue, Runze Li |
| 2025 | Understanding the Unfairness in Network Quantization. Bing Liu, Wenjun Miao, Boyu Zhang, Qiankun Zhang, Bin Yuan, Jing Wang, Shenghao Liu, Xianjun Deng |
| 2025 | Understanding the difficulties of posterior predictive estimation. Abhinav Agrawal, Justin Domke |
| 2025 | UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control. Kaizhen Zhu, Mokai Pan, Yuexin Ma, Yanwei Fu, Jingyi Yu, Jingya Wang, Ye Shi |
| 2025 | UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation. Qin Guo, Ailing Zeng, Dongxu Yue, Ceyuan Yang, Yang Cao, Hanzhong Guo, Fei Shen, Wei Liu, Xihui Liu, Dan Xu |
| 2025 | UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation. Wangzhi Zhan, Jianpeng Chen, Dongqi Fu, Dawei Zhou |
| 2025 | UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design. Xiangzhe Kong, Zishen Zhang, Ziting Zhang, Rui Jiao, Jianzhu Ma, Wenbing Huang, Kai Liu, Yang Liu |
| 2025 | UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules. Ziyang Yu, Wenbing Huang, Yang Liu |
| 2025 | Unifews: You Need Fewer Operations for Efficient Graph Neural Networks. Ningyi Liao, Zihao Yu, Ruixiao Zeng, Siqiang Luo |
| 2025 | Unified Analysis of Continuous Weak Features Learning with Applications to Learning from Missing Data. Kosuke Sugiyama, Masato Uchida |
| 2025 | Unified Breakdown Analysis for Byzantine Robust Gossip. Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx |
| 2025 | Unified K-Means Clustering with Label-Guided Manifold Learning. Qianqian Wang, Mengping Jiang, Zhengming Ding, Quanxue Gao |
| 2025 | Unified Screening for Multiple Diseases. Yigit Narter, Alihan Hüyük, Mihaela van der Schaar, Cem Tekin |
| 2025 | Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means. Mikael Møller Høgsgaard, Andrea Paudice |
| 2025 | Unifying 2D and 3D Vision-Language Understanding. Ayush Jain, Alexander Swerdlow, Yuzhou Wang, Sergio Arnaud, Ada Martin, Alexander Sax, Franziska Meier, Katerina Fragkiadaki |
| 2025 | Unifying Knowledge from Diverse Datasets to Enhance Spatial-Temporal Modeling: A Granularity-Adaptive Geographical Embedding Approach. Zhigaoyuan Wang, Ying Sun, Hengshu Zhu |
| 2025 | Unifying Specialized Visual Encoders for Video Language Models. Jihoon Chung, Tyler Zhu, Max Gonzalez Saez-Diez, Juan Carlos Niebles, Honglu Zhou, Olga Russakovsky |
| 2025 | Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers. Hang Zhou, Yuezhou Ma, Haixu Wu, Haowen Wang, Mingsheng Long |
| 2025 | Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems. Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu |
| 2025 | Universal Approximation Theorem of Deep Q-Networks. Qian Qi |
| 2025 | Universal Approximation of Mean-Field Models via Transformers. Shiba Biswal, Karthik Elamvazhuthi, Rishi Sonthalia |
| 2025 | Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing. Zijie Qiu, Jiaqi Wei, Xiang Zhang, Sheng Xu, Kai Zou, Zhi Jin, Zhiqiang Gao, Nanqing Dong, Siqi Sun |
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| 2025 | Universal Neural Optimal Transport. Jonathan Geuter, Gregor Kornhardt, Ingimar Tomasson, Vaios Laschos |
| 2025 | Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment. Harrish Thasarathan, Julian Forsyth, Thomas Fel, Matthew Kowal, Konstantinos G. Derpanis |
| 2025 | Unlocking Post-hoc Dataset Inference with Synthetic Data. Bihe Zhao, Pratyush Maini, Franziska Boenisch, Adam Dziedzic |
| 2025 | Unlocking the Capabilities of Large Vision-Language Models for Generalizable and Explainable Deepfake Detection. Peipeng Yu, Jianwei Fei, Hui Gao, Xuan Feng, Zhihua Xia, Chip-Hong Chang |
| 2025 | Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective. Junze Deng, Qinhang Wu, Peizhong Ju, Sen Lin, Yingbin Liang, Ness B. Shroff |
| 2025 | Unlocking the Power of SAM 2 for Few-Shot Segmentation. Qianxiong Xu, Lanyun Zhu, Xuanyi Liu, Guosheng Lin, Cheng Long, Ziyue Li, Rui Zhao |
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| 2025 | Unpaired Point Cloud Completion via Unbalanced Optimal Transport. Taekyung Lee, Jaemoo Choi, Jaewoong Choi, Myungjoo Kang |
| 2025 | Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments. Qianglin Wen, Chengchun Shi, Ying Yang, Niansheng Tang, Hongtu Zhu |
| 2025 | Unsupervised Learning for Class Distribution Mismatch. Pan Du, Wangbo Zhao, Xinai Lu, Nian Liu, Zhikai Li, Chaoyu Gong, Suyun Zhao, Hong Chen, Cuiping Li, Kai Wang, Yang You |
| 2025 | Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors. Shuangpeng Han, Mengmi Zhang |
| 2025 | Unveiling Markov heads in Pretrained Language Models for Offline Reinforcement Learning. Wenhao Zhao, Qiushui Xu, Linjie Xu, Lei Song, Jinyu Wang, Chunlai Zhou, Jiang Bian |
| 2025 | Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities. Ruchika Chavhan, Abhinav Mehrotra, Malcolm Chadwick, Alberto Gil Couto Pimentel Ramos, Luca Morreale, Mehdi Noroozi, Sourav Bhattacharya |
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| 2025 | VCT: Training Consistency Models with Variational Noise Coupling. Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai, Yuhta Takida, Yuki Mitsufuji |
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| 2025 | VTGaussian-SLAM: RGBD SLAM for Large Scale Scenes with Splatting View-Tied 3D Gaussians. Pengchong Hu, Zhizhong Han |
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| 2025 | Variational Control for Guidance in Diffusion Models. Kushagra Pandey, Farrin Marouf Sofian, Felix Draxler, Theofanis Karaletsos, Stephan Mandt |
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| 2025 | Wasserstein Policy Optimization. David Pfau, Ian Davies, Diana L. Borsa, João Guilherme Madeira Araújo, Brendan D. Tracey, Hado van Hasselt |
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| 2025 | WeGeFT: Weight‑Generative Fine-Tuning for Multi-Faceted Efficient Adaptation of Large Models. Chinmay Savadikar, Xi Song, Tianfu Wu |
| 2025 | Weak-to-Strong Generalization Even in Random Feature Networks, Provably. Marko Medvedev, Kaifeng Lyu, Dingli Yu, Sanjeev Arora, Zhiyuan Li, Nathan Srebro |
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| 2025 | Weakly Supervised Anomaly Detection via Dual-Tailed Kernel. Walid Durani, Tobias Nitzl, Claudia Plant, Christian Böhm |
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| 2025 | When Bad Data Leads to Good Models. Kenneth Li, Yida Chen, Fernanda B. Viégas, Martin Wattenberg |
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| 2025 | When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need. Ziming Hong, Runnan Chen, Zengmao Wang, Bo Han, Bo Du, Tongliang Liu |
| 2025 | When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets. Chen Zeno, Hila Manor, Greg Ongie, Nir Weinberger, Tomer Michaeli, Daniel Soudry |
| 2025 | When Do LLMs Help With Node Classification? A Comprehensive Analysis. Xixi Wu, Yifei Shen, Fangzhou Ge, Caihua Shan, Yizhu Jiao, Xiangguo Sun, Hong Cheng |
| 2025 | When Dynamic Data Selection Meets Data Augmentation: Achieving Enhanced Training Acceleration. Suorong Yang, Peng Ye, Furao Shen, Dongzhan Zhou |
| 2025 | When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network. Dong Xiao, Guangyao Chen, Peixi Peng, Yangru Huang, Yifan Zhao, Yongxing Dai, Yonghong Tian |
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| 2025 | When and How Does CLIP Enable Domain and Compositional Generalization? Elias Kempf, Simon Schrodi, Max Argus, Thomas Brox |
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| 2025 | Whitened CLIP as a Likelihood Surrogate of Images and Captions. Roy Betser, Meir Yossef Levi, Guy Gilboa |
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| 2025 | Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg. Like Jian, Dong Liu |
| 2025 | WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs. Lukas Thede, Karsten Roth, Matthias Bethge, Zeynep Akata, Thomas Hartvigsen |
| 2025 | WildChat-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training. Benjamin Feuer, Chinmay Hegde |
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| 2025 | Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning. Sunwoo Lee, Jaebak Hwang, Yonghyeon Jo, Seungyul Han |
| 2025 | World Model Implanting for Test-time Adaptation of Embodied Agents. Minjong Yoo, Jinwoo Jang, Sihyung Yoon, Honguk Woo |
| 2025 | WorldSimBench: Towards Video Generation Models as World Simulators. Yiran Qin, Zhelun Shi, Jiwen Yu, Xijun Wang, Enshen Zhou, Lijun Li, Zhenfei Yin, Xihui Liu, Lu Sheng, Jing Shao, Lei Bai, Ruimao Zhang |
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| 2025 | Wyckoff Transformer: Generation of Symmetric Crystals. Nikita Kazeev, Wei Nong, Ignat Romanov, Ruiming Zhu, Andrey E. Ustyuzhanin, Shuya Yamazaki, Kedar Hippalgaonkar |
| 2025 | WyckoffDiff - A Generative Diffusion Model for Crystal Symmetry. Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S. Parackal, Dong Qian, Rickard Armiento, Fredrik Lindsten |
| 2025 | X-Hacking: The Threat of Misguided AutoML. Rahul Sharma, Sumantrak Mukherjee, Andrea Sipka, Eyke Hüllermeier, Sebastian Josef Vollmer, Sergey Redyuk, David Antony Selby |
| 2025 | X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP. Hanxun Huang, Sarah Monazam Erfani, Yige Li, Xingjun Ma, James Bailey |
| 2025 | XAttention: Block Sparse Attention with Antidiagonal Scoring. Ruyi Xu, Guangxuan Xiao, Haofeng Huang, Junxian Guo, Song Han |
| 2025 | XAttnMark: Learning Robust Audio Watermarking with Cross-Attention. Yixin Liu, Lie Lu, Jihui Jin, Lichao Sun, Andrea Fanelli |
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| 2025 | You Get What You Give: Reciprocally Fair Federated Learning. Aniket Murhekar, Jiaxin Song, Parnian Shahkar, Bhaskar Ray Chaudhury, Ruta Mehta |
| 2025 | Zebra: In-Context Generative Pretraining for Solving Parametric PDEs. Louis Serrano, Armand Kassaï Koupaï, Thomas X. Wang, Pierre Erbacher, Patrick Gallinari |
| 2025 | ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning. Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark, Yejin Choi |
| 2025 | Zero Shot Generalization of Vision-Based RL Without Data Augmentation. Sumeet Batra, Gaurav S. Sukhatme |
| 2025 | Zero-Inflated Bandits. Haoyu Wei, Runzhe Wan, Lei Shi, Rui Song |
| 2025 | Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models. Farzad Farhadzadeh, Debasmit Das, Shubhankar Borse, Fatih Porikli |
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| 2025 | Zero-Shot Generalization of GNNs over Distinct Attribute Domains. Yangyi Shen, Jincheng Zhou, Beatrice Bevilacqua, Joshua Robinson, Charilaos I. Kanatsoulis, Jure Leskovec, Bruno Ribeiro |
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| 2025 | ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think. Tao Feng, Wei Li, Didi Zhu, Hangjie Yuan, Wendi Zheng, Dan Zhang, Jie Tang |
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