| 2023 | $\Lambda$-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cells. Sajad Movahedi, Melika Adabinejad, Ayyoob Imani, Arezou Keshavarz, Mostafa Dehghani, Azadeh Shakery, Babak Nadjar Araabi |
| 2023 | $\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks. Zeyu Zhu, Fanrong Li, Zitao Mo, Qinghao Hu, Gang Li, Zejian Liu, Xiaoyao Liang, Jian Cheng |
| 2023 | $k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference. Benfeng Xu, Quan Wang, Zhendong Mao, Yajuan Lyu, Qiaoqiao She, Yongdong Zhang |
| 2023 | (Certified!!) Adversarial Robustness for Free! Nicholas Carlini, Florian Tramèr, Krishnamurthy (Dj) Dvijotham, Leslie Rice, Mingjie Sun, J. Zico Kolter |
| 2023 | 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction. Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, Jianzhu Ma |
| 2023 | 3D Segmenter: 3D Transformer based Semantic Segmentation via 2D Panoramic Distillation. Zhennan Wu, Yang Li, Yifei Huang, Lin Gu, Tatsuya Harada, Hiroyuki Sato |
| 2023 | 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation. Ho Hin Lee, Shunxing Bao, Yuankai Huo, Bennett A. Landman |
| 2023 | 3D generation on ImageNet. Ivan Skorokhodov, Aliaksandr Siarohin, Yinghao Xu, Jian Ren, Hsin-Ying Lee, Peter Wonka, Sergey Tulyakov |
| 2023 | A CMDP-within-online framework for Meta-Safe Reinforcement Learning. Vanshaj Khattar, Yuhao Ding, Bilgehan Sel, Javad Lavaei, Ming Jin |
| 2023 | A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification. Paul F. Jaeger, Carsten T. Lüth, Lukas Klein, Till J. Bungert |
| 2023 | A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias. Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan |
| 2023 | A Control-Centric Benchmark for Video Prediction. Stephen Tian, Chelsea Finn, Jiajun Wu |
| 2023 | A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data. Jiajin Li, Jianheng Tang, Lemin Kong, Huikang Liu, Jia Li, Anthony Man-Cho So, Jose H. Blanchet |
| 2023 | A Differential Geometric View and Explainability of GNN on Evolving Graphs. Yazheng Liu, Xi Zhang, Sihong Xie |
| 2023 | A GNN-Guided Predict-and-Search Framework for Mixed-Integer Linear Programming. Qingyu Han, Linxin Yang, Qian Chen, Xiang Zhou, Dong Zhang, Akang Wang, Ruoyu Sun, Xiaodong Luo |
| 2023 | A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis. Damien Ferbach, Christos Tsirigotis, Gauthier Gidel, Avishek Joey Bose |
| 2023 | A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan |
| 2023 | A General Rank Preserving Framework for Asymmetric Image Retrieval. Hui Wu, Min Wang, Wengang Zhou, Houqiang Li |
| 2023 | A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps. Kiarash Jamali, Dari Kimanius, Sjors H. W. Scheres |
| 2023 | A Higher Precision Algorithm for Computing the $1$-Wasserstein Distance. Pankaj K. Agarwal, Sharath Raghvendra, Pouyan Shirzadian, Rachita Sowle |
| 2023 | A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond. Yong Lin, Renjie Pi, Weizhong Zhang, Xiaobo Xia, Jiahui Gao, Xiao Zhou, Tongliang Liu, Bo Han |
| 2023 | A Kernel Perspective of Skip Connections in Convolutional Networks. Daniel Barzilai, Amnon Geifman, Meirav Galun, Ronen Basri |
| 2023 | A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation. Yingda Yin, Yang Wang, He Wang, Baoquan Chen |
| 2023 | A Learning Based Hypothesis Test for Harmful Covariate Shift. Tom Ginsberg, Zhongyuan Liang, Rahul G. Krishnan |
| 2023 | A Message Passing Perspective on Learning Dynamics of Contrastive Learning. Yifei Wang, Qi Zhang, Tianqi Du, Jiansheng Yang, Zhouchen Lin, Yisen Wang |
| 2023 | A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics. Qing Li, Siyuan Huang, Yining Hong, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu |
| 2023 | A Mixture-of-Expert Approach to RL-based Dialogue Management. Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, Dhawal Gupta, Moonkyung Ryu, Mohammad Ghavamzadeh, Craig Boutilier |
| 2023 | A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning. Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, De-Chuan Zhan |
| 2023 | A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification. Xiang Hu, Xinyu Kong, Kewei Tu |
| 2023 | A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. Liyuan Xu, Arthur Gretton |
| 2023 | A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks. Xinyi Wu, Zhengdao Chen, William Wei Wang, Ali Jadbabaie |
| 2023 | A Non-monotonic Self-terminating Language Model. Eugene Choi, Kyunghyun Cho, Cheolhyoung Lee |
| 2023 | A Primal-Dual Framework for Transformers and Neural Networks. Tan Minh Nguyen, Tam Minh Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher |
| 2023 | A Self-Attention Ansatz for Ab-initio Quantum Chemistry. Ingrid von Glehn, James S. Spencer, David Pfau |
| 2023 | A Simple Approach for Visual Room Rearrangement: 3D Mapping and Semantic Search. Brandon Trabucco, Gunnar A. Sigurdsson, Robinson Piramuthu, Gaurav S. Sukhatme, Ruslan Salakhutdinov |
| 2023 | A Simple Yet Powerful Deep Active Learning With Snapshots Ensembles. Seohyeon Jung, Sanghyun Kim, Juho Lee |
| 2023 | A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks. Marc Anton Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson |
| 2023 | A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi |
| 2023 | A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation. Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo, Shixiang Shane Gu |
| 2023 | A Theoretical Framework for Inference and Learning in Predictive Coding Networks. Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz, Rafal Bogacz |
| 2023 | A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity. Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen |
| 2023 | A Theory of Dynamic Benchmarks. Ali Shirali, Rediet Abebe, Moritz Hardt |
| 2023 | A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam |
| 2023 | A Unified Algebraic Perspective on Lipschitz Neural Networks. Alexandre Araujo, Aaron J. Havens, Blaise Delattre, Alexandre Allauzen, Bin Hu |
| 2023 | A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer |
| 2023 | A Unified Framework for Soft Threshold Pruning. Yanqi Chen, Zhengyu Ma, Wei Fang, Xiawu Zheng, Zhaofei Yu, Yonghong Tian |
| 2023 | A VAE for Transformers with Nonparametric Variational Information Bottleneck. James Henderson, Fabio Fehr |
| 2023 | A View From Somewhere: Human-Centric Face Representations. Jerone Theodore Alexander Andrews, Przemyslaw Joniak, Alice Xiang |
| 2023 | A critical look at the evaluation of GNNs under heterophily: Are we really making progress? Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, Liudmila Prokhorenkova |
| 2023 | A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet. Ido Galil, Mohammed Dabbah, Ran El-Yaniv |
| 2023 | A law of adversarial risk, interpolation, and label noise. Daniel Paleka, Amartya Sanyal |
| 2023 | A new characterization of the edge of stability based on a sharpness measure aware of batch gradient distribution. Sungyoon Lee, Cheongjae Jang |
| 2023 | A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation. Edoardo Balzani, Jean-Paul Noel, Pedro Herrero-Vidal, Dora E. Angelaki, Cristina Savin |
| 2023 | A theoretical study of inductive biases in contrastive learning. Jeff Z. HaoChen, Tengyu Ma |
| 2023 | A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta. Maksim Velikanov, Denis Kuznedelev, Dmitry Yarotsky |
| 2023 | AANG : Automating Auxiliary Learning. Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar |
| 2023 | ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks. Yuelin Wang, Kai Yi, Xinliang Liu, Yu Guang Wang, Shi Jin |
| 2023 | AE-FLOW: Autoencoders with Normalizing Flows for Medical Images Anomaly Detection. Yuzhong Zhao, Qiaoqiao Ding, Xiaoqun Zhang |
| 2023 | AGRO: Adversarial discovery of error-prone Groups for Robust Optimization. Bhargavi Paranjape, Pradeep Dasigi, Vivek Srikumar, Luke Zettlemoyer, Hannaneh Hajishirzi |
| 2023 | AIM: Adapting Image Models for Efficient Video Action Recognition. Taojiannan Yang, Yi Zhu, Yusheng Xie, Aston Zhang, Chen Chen, Mu Li |
| 2023 | Accelerated Single-Call Methods for Constrained Min-Max Optimization. Yang Cai, Weiqiang Zheng |
| 2023 | Accelerating Guided Diffusion Sampling with Splitting Numerical Methods. Suttisak Wizadwongsa, Supasorn Suwajanakorn |
| 2023 | Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time. Jun-Kun Wang, Andre Wibisono |
| 2023 | Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference. Michael Volpp, Philipp Dahlinger, Philipp Becker, Christian Daniel, Gerhard Neumann |
| 2023 | Accurate Image Restoration with Attention Retractable Transformer. Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, Xin Yuan |
| 2023 | Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats. Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry |
| 2023 | Achieve the Minimum Width of Neural Networks for Universal Approximation. Yongqiang Cai |
| 2023 | Achieving Near-Optimal Individual Regret & Low Communications in Multi-Agent Bandits. Xuchuang Wang, Lin Yang, Yu-Zhen Janice Chen, Xutong Liu, Mohammad Hajiesmaili, Don Towsley, John C. S. Lui |
| 2023 | Achieving Sub-linear Regret in Infinite Horizon Average Reward Constrained MDP with Linear Function Approximation. Arnob Ghosh, Xingyu Zhou, Ness B. Shroff |
| 2023 | Actionable Neural Representations: Grid Cells from Minimal Constraints. Will Dorrell, Peter E. Latham, Tim E. J. Behrens, James C. R. Whittington |
| 2023 | Active Image Indexing. Pierre Fernandez, Matthijs Douze, Hervé Jégou, Teddy Furon |
| 2023 | Active Learning for Object Detection with Evidential Deep Learning and Hierarchical Uncertainty Aggregation. Younghyun Park, Wonjeong Choi, Soyeong Kim, Dong-Jun Han, Jaekyun Moon |
| 2023 | Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle. Jae Oh Woo |
| 2023 | Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao |
| 2023 | Adaptive Optimization in the ∞-Width Limit. Etai Littwin, Greg Yang |
| 2023 | Adaptive Robust Evidential Optimization For Open Set Detection from Imbalanced Data. Hitesh Sapkota, Qi Yu |
| 2023 | Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation. Marius-Constantin Dinu, Markus Holzleitner, Maximilian Beck, Hoan Duc Nguyen, Andrea Huber, Hamid Eghbal-zadeh, Bernhard Alois Moser, Sergei V. Pereverzyev, Sepp Hochreiter, Werner Zellinger |
| 2023 | Advancing Radiograph Representation Learning with Masked Record Modeling. Hong-Yu Zhou, Chenyu Lian, Liansheng Wang, Yizhou Yu |
| 2023 | Adversarial Attacks on Adversarial Bandits. Yuzhe Ma, Zhijin Zhou |
| 2023 | Adversarial Diversity in Hanabi. Brandon Cui, Andrei Lupu, Samuel Sokota, Hengyuan Hu, David J. Wu, Jakob Nicolaus Foerster |
| 2023 | Adversarial Imitation Learning with Preferences. Aleksandar Taranovic, Andras Gabor Kupcsik, Niklas Freymuth, Gerhard Neumann |
| 2023 | Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks. Zhiyuan Cheng, James Liang, Guanhong Tao, Dongfang Liu, Xiangyu Zhang |
| 2023 | Agent-based Graph Neural Networks. Karolis Martinkus, Pál András Papp, Benedikt Schesch, Roger Wattenhofer |
| 2023 | Agnostic Learning of General ReLU Activation Using Gradient Descent. Pranjal Awasthi, Alex Tang, Aravindan Vijayaraghavan |
| 2023 | Agree to Disagree: Diversity through Disagreement for Better Transferability. Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy |
| 2023 | Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. Joel Dapello, Kohitij Kar, Martin Schrimpf, Robert Baldwin Geary, Michael Ferguson, David Daniel Cox, James J. DiCarlo |
| 2023 | Almost Linear Constant-Factor Sketching for $\ell_1$ and Logistic Regression. Alexander Munteanu, Simon Omlor, David P. Woodruff |
| 2023 | Alternating Differentiation for Optimization Layers. Haixiang Sun, Ye Shi, Jingya Wang, Hoang Duong Tuan, H. Vincent Poor, Dacheng Tao |
| 2023 | Amortised Invariance Learning for Contrastive Self-Supervision. Ruchika Chavhan, Jan Stuehmer, Calum Heggan, Mehrdad Yaghoobi, Timothy M. Hospedales |
| 2023 | An Adaptive Policy to Employ Sharpness-Aware Minimization. Weisen Jiang, Hansi Yang, Yu Zhang, James T. Kwok |
| 2023 | An Additive Instance-Wise Approach to Multi-class Model Interpretation. Vy Vo, Van Nguyen, Trung Le, Quan Hung Tran, Gholamreza Haffari, Seyit Camtepe, Dinh Phung |
| 2023 | An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation. Yuqiao Wen, Yongchang Hao, Yanshuai Cao, Lili Mou |
| 2023 | An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network. Amit Daniely, Elad Granot |
| 2023 | An Extensible Multi-modal Multi-task Object Dataset with Materials. Trevor Scott Standley, Ruohan Gao, Dawn Chen, Jiajun Wu, Silvio Savarese |
| 2023 | An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion. Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit Haim Bermano, Gal Chechik, Daniel Cohen-Or |
| 2023 | An efficient encoder-decoder architecture with top-down attention for speech separation. Kai Li, Runxuan Yang, Xiaolin Hu |
| 2023 | Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning. Ting Chen, Ruixiang Zhang, Geoffrey E. Hinton |
| 2023 | Analogy-Forming Transformers for Few-Shot 3D Parsing. Nikolaos Gkanatsios, Mayank Singh, Zhaoyuan Fang, Shubham Tulsiani, Katerina Fragkiadaki |
| 2023 | Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel. Ryuichi Kanoh, Mahito Sugiyama |
| 2023 | Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections. Edward De Brouwer, Rahul G. Krishnan |
| 2023 | Anisotropic Message Passing: Graph Neural Networks with Directional and Long-Range Interactions. Moritz Thürlemann, Sereina Riniker |
| 2023 | Anti-Symmetric DGN: a stable architecture for Deep Graph Networks. Alessio Gravina, Davide Bacciu, Claudio Gallicchio |
| 2023 | Any-scale Balanced Samplers for Discrete Space. Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai |
| 2023 | AnyDA: Anytime Domain Adaptation. Omprakash Chakraborty, Aadarsh Sahoo, Rameswar Panda, Abir Das |
| 2023 | Approximate Bayesian Inference with Stein Functional Variational Gradient Descent. Tobias Pielok, Bernd Bischl, David Rügamer |
| 2023 | Approximate Nearest Neighbor Search through Modern Error-Correcting Codes. Noam Touitou, Nissim Halabi |
| 2023 | Approximate Vanishing Ideal Computations at Scale. Elias Samuel Wirth, Hiroshi Kera, Sebastian Pokutta |
| 2023 | Approximation and non-parametric estimation of functions over high-dimensional spheres via deep ReLU networks. Namjoon Suh, Tian-Yi Zhou, Xiaoming Huo |
| 2023 | ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations. Xuyang Zhao, Tianqi Du, Yisen Wang, Jun Yao, Weiran Huang |
| 2023 | Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing. Yu Liu, Mingbo Zhao, Zhao Zhang, Jicong Fan, Yang Lou, Shuicheng Yan |
| 2023 | Are More Layers Beneficial to Graph Transformers? Haiteng Zhao, Shuming Ma, Dongdong Zhang, Zhi-Hong Deng, Furu Wei |
| 2023 | Artificial Neuronal Ensembles with Learned Context Dependent Gating. Matthew J. Tilley, Michelle Miller, David Freedman |
| 2023 | Ask Me Anything: A simple strategy for prompting language models. Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel J. Orr, Neel Guha, Kush Bhatia, Ines Chami, Christopher Ré |
| 2023 | Associative Memory Augmented Asynchronous Spatiotemporal Representation Learning for Event-based Perception. Uday Kamal, Saurabh Dash, Saibal Mukhopadhyay |
| 2023 | Asymptotic Instance-Optimal Algorithms for Interactive Decision Making. Kefan Dong, Tengyu Ma |
| 2023 | Asynchronous Distributed Bilevel Optimization. Yang Jiao, Kai Yang, Tiancheng Wu, Dongjin Song, Chengtao Jian |
| 2023 | Asynchronous Gradient Play in Zero-Sum Multi-agent Games. Ruicheng Ao, Shicong Cen, Yuejie Chi |
| 2023 | AudioGen: Textually Guided Audio Generation. Felix Kreuk, Gabriel Synnaeve, Adam Polyak, Uriel Singer, Alexandre Défossez, Jade Copet, Devi Parikh, Yaniv Taigman, Yossi Adi |
| 2023 | Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps. Lu Han, Han-Jia Ye, De-Chuan Zhan |
| 2023 | Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji |
| 2023 | Auto-Encoding Goodness of Fit. Aaron Palmer, Zhiyi Chi, Derek Aguiar, Jinbo Bi |
| 2023 | AutoGT: Automated Graph Transformer Architecture Search. Zizhao Zhang, Xin Wang, Chaoyu Guan, Ziwei Zhang, Haoyang Li, Wenwu Zhu |
| 2023 | AutoTransfer: AutoML with Knowledge Transfer - An Application to Graph Neural Networks. Kaidi Cao, Jiaxuan You, Jiaju Liu, Jure Leskovec |
| 2023 | Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning? Runpei Dong, Zekun Qi, Linfeng Zhang, Junbo Zhang, Jianjian Sun, Zheng Ge, Li Yi, Kaisheng Ma |
| 2023 | Automated Data Augmentations for Graph Classification. Youzhi Luo, Michael McThrow, Wing Yee Au, Tao Komikado, Kanji Uchino, Koji Maruhashi, Shuiwang Ji |
| 2023 | Automatic Chain of Thought Prompting in Large Language Models. Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola |
| 2023 | Automating Nearest Neighbor Search Configuration with Constrained Optimization. Philip Sun, Ruiqi Guo, Sanjiv Kumar |
| 2023 | Autoregressive Conditional Neural Processes. Wessel P. Bruinsma, Stratis Markou, James Requeima, Andrew Y. K. Foong, Tom R. Andersson, Anna Vaughan, Anthony Buonomo, J. Scott Hosking, Richard E. Turner |
| 2023 | Average Sensitivity of Decision Tree Learning. Satoshi Hara, Yuichi Yoshida |
| 2023 | Avoiding spurious correlations via logit correction. Sheng Liu, Xu Zhang, Nitesh Sekhar, Yue Wu, Prateek Singhal, Carlos Fernandez-Granda |
| 2023 | BALTO: fast tensor program optimization with diversity-based active learning. Jun Bi, Xiaqing Li, Qi Guo, Rui Zhang, Yuanbo Wen, Xing Hu, Zidong Du, Xinkai Song, Yifan Hao, Yunji Chen |
| 2023 | BC-IRL: Learning Generalizable Reward Functions from Demonstrations. Andrew Szot, Amy Zhang, Dhruv Batra, Zsolt Kira, Franziska Meier |
| 2023 | BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion. Fu-Yun Wang, Da-Wei Zhou, Liu Liu, Han-Jia Ye, Yatao Bian, De-Chuan Zhan, Peilin Zhao |
| 2023 | BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection. Zehui Chen, Zhenyu Li, Shiquan Zhang, Liangji Fang, Qinhong Jiang, Feng Zhao |
| 2023 | BSTT: A Bayesian Spatial-Temporal Transformer for Sleep Staging. Yuchen Liu, Ziyu Jia |
| 2023 | Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning. Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz, Rafal Bogacz |
| 2023 | Backpropagation through Combinatorial Algorithms: Identity with Projection Works. Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, Georg Martius |
| 2023 | Backstepping Temporal Difference Learning. Han-Dong Lim, Donghwan Lee |
| 2023 | Bag of Tricks for Unsupervised Text-to-Speech. Yi Ren, Chen Zhang, Shuicheng Yan |
| 2023 | Basic Binary Convolution Unit for Binarized Image Restoration Network. Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, Luc Van Gool |
| 2023 | Batch Multivalid Conformal Prediction. Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth |
| 2023 | Bayes Risk CTC: Controllable CTC Alignment in Sequence-to-Sequence Tasks. Jinchuan Tian, Brian Yan, Jianwei Yu, Chao Weng, Dong Yu, Shinji Watanabe |
| 2023 | Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images. Yufei Cui, Ziquan Liu, Xiangyu Liu, Xue Liu, Cong Wang, Tei-Wei Kuo, Chun Jason Xue, Antoni B. Chan |
| 2023 | Bayesian Oracle for bounding information gain in neural encoding models. Konstantin-Klemens Lurz, Mohammad Bashiri, Edgar Y. Walker, Fabian H. Sinz |
| 2023 | Become a Proficient Player with Limited Data through Watching Pure Videos. Weirui Ye, Yunsheng Zhang, Pieter Abbeel, Yang Gao |
| 2023 | Behavior Prior Representation learning for Offline Reinforcement Learning. Hongyu Zang, Xin Li, Jie Yu, Chen Liu, Riashat Islam, Remi Tachet des Combes, Romain Laroche |
| 2023 | Behavior Proximal Policy Optimization. Zifeng Zhuang, Kun Lei, Jinxin Liu, Donglin Wang, Yilang Guo |
| 2023 | Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition. Jianhao Ma, Lingjun Guo, Salar Fattahi |
| 2023 | Benchmarking Constraint Inference in Inverse Reinforcement Learning. Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart |
| 2023 | Benchmarking Offline Reinforcement Learning on Real-Robot Hardware. Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wuthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius |
| 2023 | Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models. Kaiyue Wen, Jiaye Teng, Jingzhao Zhang |
| 2023 | Better Generative Replay for Continual Federated Learning. Daiqing Qi, Handong Zhao, Sheng Li |
| 2023 | Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation. Martin Zong, Zengyu Qiu, Xinzhu Ma, Kunlin Yang, Chunya Liu, Jun Hou, Shuai Yi, Wanli Ouyang |
| 2023 | Betty: An Automatic Differentiation Library for Multilevel Optimization. Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing |
| 2023 | Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD. Konstantinos E. Nikolakakis, Farzin Haddadpour, Amin Karbasi, Dionysios S. Kalogerias |
| 2023 | Beyond calibration: estimating the grouping loss of modern neural networks. Alexandre Perez-Lebel, Marine Le Morvan, Gaël Varoquaux |
| 2023 | Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients. Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng |
| 2023 | Bias Propagation in Federated Learning. Hongyan Chang, Reza Shokri |
| 2023 | Bidirectional Language Models Are Also Few-shot Learners. Ajay Patel, Bryan Li, Mohammad Sadegh Rasooli, Noah Constant, Colin Raffel, Chris Callison-Burch |
| 2023 | BigVGAN: A Universal Neural Vocoder with Large-Scale Training. Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon |
| 2023 | Binding Language Models in Symbolic Languages. Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Tao Yu |
| 2023 | Bispectral Neural Networks. Sophia Sanborn, Christian Shewmake, Bruno A. Olshausen, Christopher J. Hillar |
| 2023 | Bit-Pruning: A Sparse Multiplication-Less Dot-Product. Yusuke Sekikawa, Shingo Yashima |
| 2023 | Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine |
| 2023 | Block and Subword-Scaling Floating-Point (BSFP) : An Efficient Non-Uniform Quantization For Low Precision Inference. Yun-Chen Lo, Tse-Kuang Lee, Ren-Shuo Liu |
| 2023 | Blurring Diffusion Models. Emiel Hoogeboom, Tim Salimans |
| 2023 | Boosting Adversarial Transferability using Dynamic Cues. Muzammal Naseer, Ahmad Mahmood, Salman Khan, Fahad Shahbaz Khan |
| 2023 | Boosting Causal Discovery via Adaptive Sample Reweighting. An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua |
| 2023 | Boosting Multiagent Reinforcement Learning via Permutation Invariant and Permutation Equivariant Networks. Jianye Hao, Xiaotian Hao, Hangyu Mao, Weixun Wang, Yaodong Yang, Dong Li, Yan Zheng, Zhen Wang |
| 2023 | Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs. Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang |
| 2023 | Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint. Borui Zhang, Wenzhao Zheng, Jie Zhou, Jiwen Lu |
| 2023 | Brain-like representational straightening of natural movies in robust feedforward neural networks. Tahereh Toosi, Elias B. Issa |
| 2023 | BrainBERT: Self-supervised representation learning for intracranial recordings. Christopher Wang, Vighnesh Subramaniam, Adam Uri Yaari, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu |
| 2023 | Breaking Correlation Shift via Conditional Invariant Regularizer. Mingyang Yi, Ruoyu Wang, Jiacheng Sun, Zhenguo Li, Zhi-Ming Ma |
| 2023 | Bridge the Inference Gaps of Neural Processes via Expectation Maximization. Qi Wang, Marco Federici, Herke van Hoof |
| 2023 | Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes. Zecheng Hao, Jianhao Ding, Tong Bu, Tiejun Huang, Zhaofei Yu |
| 2023 | Bridging the Gap to Real-World Object-Centric Learning. Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, Francesco Locatello |
| 2023 | Broken Neural Scaling Laws. Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger |
| 2023 | Budgeted Training for Vision Transformer. Zhuofan Xia, Xuran Pan, Xuan Jin, Yuan He, Hui Xue, Shiji Song, Gao Huang |
| 2023 | Building Normalizing Flows with Stochastic Interpolants. Michael S. Albergo, Eric Vanden-Eijnden |
| 2023 | Building a Subspace of Policies for Scalable Continual Learning. Jean-Baptiste Gaya, Thang Doan, Lucas Caccia, Laure Soulier, Ludovic Denoyer, Roberta Raileanu |
| 2023 | CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning. Samuel Maddock, Alexandre Sablayrolles, Pierre Stock |
| 2023 | CASR: Generating Complex Sequences with Autoregressive Self-Boost Refinement. Hongwei Han, Mengyu Zhou, Shi Han, Xiu Li, Dongmei Zhang |
| 2023 | CFlowNets: Continuous Control with Generative Flow Networks. Yinchuan Li, Shuang Luo, Haozhi Wang, Jianye Hao |
| 2023 | CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning. Sheng Yue, Guanbo Wang, Wei Shao, Zhaofeng Zhang, Sen Lin, Ju Ren, Junshan Zhang |
| 2023 | CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks. Tuomas P. Oikarinen, Tsui-Wei Weng |
| 2023 | CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Alignment. Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, Jiebo Luo |
| 2023 | CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos. Hao-Wen Dong, Naoya Takahashi, Yuki Mitsufuji, Julian J. McAuley, Taylor Berg-Kirkpatrick |
| 2023 | CO3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving. Runjian Chen, Yao Mu, Runsen Xu, Wenqi Shao, Chenhan Jiang, Hang Xu, Yu Qiao, Zhenguo Li, Ping Luo |
| 2023 | CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations. Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, G. A. Pershing, Henrique Teles Maia, Maurizio M. Chiaramonte, Kevin T. Carlberg, Eitan Grinspun |
| 2023 | CUDA: Curriculum of Data Augmentation for Long-tailed Recognition. Sumyeong Ahn, Jongwoo Ko, Se-Young Yun |
| 2023 | CUTS: Neural Causal Discovery from Irregular Time-Series Data. Yuxiao Cheng, Runzhao Yang, Tingxiong Xiao, Zongren Li, Jinli Suo, Kunlun He, Qionghai Dai |
| 2023 | Calibrating Sequence likelihood Improves Conditional Language Generation. Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, Peter J. Liu |
| 2023 | Calibrating Transformers via Sparse Gaussian Processes. Wenlong Chen, Yingzhen Li |
| 2023 | Calibrating the Rigged Lottery: Making All Tickets Reliable. Bowen Lei, Ruqi Zhang, Dongkuan Xu, Bani K. Mallick |
| 2023 | Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems. Yewen Fan, Nian Si, Kun Zhang |
| 2023 | Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories. Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh |
| 2023 | Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study. Mingxu Tao, Yansong Feng, Dongyan Zhao |
| 2023 | Can CNNs Be More Robust Than Transformers? Zeyu Wang, Yutong Bai, Yuyin Zhou, Cihang Xie |
| 2023 | Can Neural Networks Learn Implicit Logic from Physical Reasoning? Aaron Traylor, Roman Feiman, Ellie Pavlick |
| 2023 | Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN? Jie Ren, Zhanpeng Zhou, Qirui Chen, Quanshi Zhang |
| 2023 | Can We Find Nash Equilibria at a Linear Rate in Markov Games? Zhuoqing Song, Jason D. Lee, Zhuoran Yang |
| 2023 | Can discrete information extraction prompts generalize across language models? Nathanaël Carraz Rakotonirina, Roberto Dessì, Fabio Petroni, Sebastian Riedel, Marco Baroni |
| 2023 | Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. Yuxin Wen, Arpit Bansal, Hamid Kazemi, Eitan Borgnia, Micah Goldblum, Jonas Geiping, Tom Goldstein |
| 2023 | Capturing the Motion of Every Joint: 3D Human Pose and Shape Estimation with Independent Tokens. Sen Yang, Wen Heng, Gang Liu, Guozhong Luo, Wankou Yang, Gang Yu |
| 2023 | Causal Balancing for Domain Generalization. Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang |
| 2023 | Causal Confusion and Reward Misidentification in Preference-Based Reward Learning. Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca D. Dragan, Daniel S. Brown |
| 2023 | Causal Estimation for Text Data with (Apparent) Overlap Violations. Lin Gui, Victor Veitch |
| 2023 | Causal Imitation Learning via Inverse Reinforcement Learning. Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim |
| 2023 | Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang |
| 2023 | Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems. Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves |
| 2023 | Causality Compensated Attention for Contextual Biased Visual Recognition. Ruyang Liu, Jingjia Huang, Thomas H. Li, Ge Li |
| 2023 | Certifiably Robust Policy Learning against Adversarial Multi-Agent Communication. Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh, Yongyuan Liang, Soheil Feizi, Sumitra Ganesh, Furong Huang |
| 2023 | Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation. Maksym Yatsura, Kaspar Sakmann, N. Grace Hua, Matthias Hein, Jan Hendrik Metzen |
| 2023 | Certified Training: Small Boxes are All You Need. Mark Niklas Müller, Franziska Eckert, Marc Fischer, Martin T. Vechev |
| 2023 | Characteristic Neural Ordinary Differential Equation. Xingzi Xu, Ali Hasan, Khalil Elkhalil, Jie Ding, Vahid Tarokh |
| 2023 | Characterizing intrinsic compositionality in transformers with Tree Projections. Shikhar Murty, Pratyusha Sharma, Jacob Andreas, Christopher D. Manning |
| 2023 | Characterizing the Influence of Graph Elements. Zizhang Chen, Peizhao Li, Hongfu Liu, Pengyu Hong |
| 2023 | Characterizing the spectrum of the NTK via a power series expansion. Michael Murray, Hui Jin, Benjamin Bowman, Guido Montúfar |
| 2023 | Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization. Chunhui Zhang, Yijun Tian, Mingxuan Ju, Zheyuan Liu, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang |
| 2023 | Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning. Yat Long Lo, Christian Schröder de Witt, Samuel Sokota, Jakob Nicolaus Foerster, Shimon Whiteson |
| 2023 | ChiroDiff: Modelling chirographic data with Diffusion Models. Ayan Das, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song |
| 2023 | ChordMixer: A Scalable Neural Attention Model for Sequences with Different Length. Ruslan Khalitov, Tong Yu, Lei Cheng, Zhirong Yang |
| 2023 | Choreographer: Learning and Adapting Skills in Imagination. Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste, Sai Rajeswar |
| 2023 | CircNet: Meshing 3D Point Clouds with Circumcenter Detection. Huan Lei, Ruitao Leng, Liang Zheng, Hongdong Li |
| 2023 | CktGNN: Circuit Graph Neural Network for Electronic Design Automation. Zehao Dong, Weidong Cao, Muhan Zhang, Dacheng Tao, Yixin Chen, Xuan Zhang |
| 2023 | Classically Approximating Variational Quantum Machine Learning with Random Fourier Features. Jonas Landman, Slimane Thabet, Constantin Dalyac, Hela Mhiri, Elham Kashefi |
| 2023 | Clean-image Backdoor: Attacking Multi-label Models with Poisoned Labels Only. Kangjie Chen, Xiaoxuan Lou, Guowen Xu, Jiwei Li, Tianwei Zhang |
| 2023 | Clifford Neural Layers for PDE Modeling. Johannes Brandstetter, Rianne van den Berg, Max Welling, Jayesh K. Gupta |
| 2023 | CoRTX: Contrastive Framework for Real-time Explanation. Yu-Neng Chuang, Guanchu Wang, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Ben Hu |
| 2023 | Code Translation with Compiler Representations. Marc Szafraniec, Baptiste Rozière, Hugh Leather, Patrick Labatut, François Charton, Gabriel Synnaeve |
| 2023 | CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code. Nadezhda Chirkova, Sergey Troshin |
| 2023 | CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis. Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong |
| 2023 | CodeT: Code Generation with Generated Tests. Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen |
| 2023 | CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers. Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, Jie Tang |
| 2023 | Collaborative Pure Exploration in Kernel Bandit. Yihan Du, Wei Chen, Yuko Kuroki, Longbo Huang |
| 2023 | Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han |
| 2023 | Combinatorial Pure Exploration of Causal Bandits. Nuoya Xiong, Wei Chen |
| 2023 | Combinatorial-Probabilistic Trade-Off: P-Values of Community Properties Test in the Stochastic Block Models. Shuting Shen, Junwei Lu |
| 2023 | Competitive Physics Informed Networks. Qi Zeng, Yash Kothari, Spencer H. Bryngelson, Florian Schäfer |
| 2023 | Complexity-Based Prompting for Multi-step Reasoning. Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, Tushar Khot |
| 2023 | Composing Ensembles of Pre-trained Models via Iterative Consensus. Shuang Li, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba, Igor Mordatch |
| 2023 | Composing Task Knowledge With Modular Successor Feature Approximators. Wilka Carvalho, Angelos Filos, Richard L. Lewis, Honglak Lee, Satinder Singh |
| 2023 | Composite Slice Transformer: An Efficient Transformer with Composition of Multi-Scale Multi-Range Attentions. Mingu Lee, Saurabh Pitre, Tianyu Jiang, Pierre-David Letourneau, Matthew J. Morse, Kanghwan Jang, Joseph Soriaga, Parham Noorzad, Hsin-Pai Cheng, Christopher Lott |
| 2023 | Compositional Law Parsing with Latent Random Functions. Fan Shi, Bin Li, Xiangyang Xue |
| 2023 | Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection. Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun |
| 2023 | Compositional Semantic Parsing with Large Language Models. Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, Denny Zhou |
| 2023 | Compositional Task Representations for Large Language Models. Nan Shao, Zefan Cai, Hanwei Xu, Chonghua Liao, Yanan Zheng, Zhilin Yang |
| 2023 | Compositionality with Variation Reliably Emerges in Neural Networks. Henry Conklin, Kenny Smith |
| 2023 | Compressing multidimensional weather and climate data into neural networks. Langwen Huang, Torsten Hoefler |
| 2023 | Computational Language Acquisition with Theory of Mind. Andy Liu, Hao Zhu, Emmy Liu, Yonatan Bisk, Graham Neubig |
| 2023 | Computing all Optimal Partial Transports. Abhijeet Phatak, Sharath Raghvendra, Chittaranjan Tripathy, Kaiyi Zhang |
| 2023 | Concept Gradient: Concept-based Interpretation Without Linear Assumption. Andrew Bai, Chih-Kuan Yeh, Neil Y. C. Lin, Pradeep Kumar Ravikumar, Cho-Jui Hsieh |
| 2023 | Concept-level Debugging of Part-Prototype Networks. Andrea Bontempelli, Stefano Teso, Katya Tentori, Fausto Giunchiglia, Andrea Passerini |
| 2023 | Conditional Antibody Design as 3D Equivariant Graph Translation. Xiangzhe Kong, Wenbing Huang, Yang Liu |
| 2023 | Conditional Positional Encodings for Vision Transformers. Xiangxiang Chu, Zhi Tian, Bo Zhang, Xinlong Wang, Chunhua Shen |
| 2023 | Confidence Estimation Using Unlabeled Data. Chen Li, Xiaoling Hu, Chao Chen |
| 2023 | Confidence-Based Feature Imputation for Graphs with Partially Known Features. Daeho Um, Jiwoong Park, Seulki Park, Jin Young Choi |
| 2023 | Confidence-Conditioned Value Functions for Offline Reinforcement Learning. Joey Hong, Aviral Kumar, Sergey Levine |
| 2023 | Confidential-PROFITT: Confidential PROof of FaIr Training of Trees. Ali Shahin Shamsabadi, Sierra Calanda Wyllie, Nicholas Franzese, Natalie Dullerud, Sébastien Gambs, Nicolas Papernot, Xiao Wang, Adrian Weller |
| 2023 | Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization. Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb, Hyunwoo Kim, Baher Abdulhai, Scott Sanner |
| 2023 | Consolidator: Mergable Adapter with Group Connections for Visual Adaptation. Tianxiang Hao, Hui Chen, Yuchen Guo, Guiguang Ding |
| 2023 | Constraining Representations Yields Models That Know What They Don't Know. João Monteiro, Pau Rodríguez, Pierre-André Noël, Issam H. Laradji, David Vázquez |
| 2023 | Constructive TT-representation of the tensors given as index interaction functions with applications. Gleb V. Ryzhakov, Ivan V. Oseledets |
| 2023 | Context-enriched molecule representations improve few-shot drug discovery. Johannes Schimunek, Philipp Seidl, Lukas Friedrich, Daniel Kuhn, Friedrich Rippmann, Sepp Hochreiter, Günter Klambauer |
| 2023 | Contextual Convolutional Networks. Shuxian Liang, Xu Shen, Tongliang Liu, Xian-Sheng Hua |
| 2023 | Contextual Image Masking Modeling via Synergized Contrasting without View Augmentation for Faster and Better Visual Pretraining. Shaofeng Zhang, Feng Zhu, Rui Zhao, Junchi Yan |
| 2023 | Contextual bandits with concave rewards, and an application to fair ranking. Virginie Do, Elvis Dohmatob, Matteo Pirotta, Alessandro Lazaric, Nicolas Usunier |
| 2023 | Continual Pre-training of Language Models. Zixuan Ke, Yijia Shao, Haowei Lin, Tatsuya Konishi, Gyuhak Kim, Bing Liu |
| 2023 | Continual Transformers: Redundancy-Free Attention for Online Inference. Lukas Hedegaard, Arian Bakhtiarnia, Alexandros Iosifidis |
| 2023 | Continual Unsupervised Disentangling of Self-Organizing Representations. Zhiyuan Li, Xiajun Jiang, Ryan Missel, Prashnna Kumar Gyawali, Nilesh Kumar, Linwei Wang |
| 2023 | Continual evaluation for lifelong learning: Identifying the stability gap. Matthias De Lange, Gido M. van de Ven, Tinne Tuytelaars |
| 2023 | Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization. Jun-Kun Wang, Andre Wibisono |
| 2023 | Continuous PDE Dynamics Forecasting with Implicit Neural Representations. Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, Patrick Gallinari |
| 2023 | Continuous pseudo-labeling from the start. Dan Berrebbi, Ronan Collobert, Samy Bengio, Navdeep Jaitly, Tatiana Likhomanenko |
| 2023 | Continuous-Discrete Convolution for Geometry-Sequence Modeling in Proteins. Hehe Fan, Zhangyang Wang, Yi Yang, Mohan S. Kankanhalli |
| 2023 | Continuous-time identification of dynamic state-space models by deep subspace encoding. Gerben Izaak Beintema, Maarten Schoukens, Roland Tóth |
| 2023 | ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond. Xiaojun Guo, Yifei Wang, Tianqi Du, Yisen Wang |
| 2023 | Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning. Zaid Khan, Yun Fu |
| 2023 | Contrastive Audio-Visual Masked Autoencoder. Yuan Gong, Andrew Rouditchenko, Alexander H. Liu, David Harwath, Leonid Karlinsky, Hilde Kuehne, James R. Glass |
| 2023 | Contrastive Corpus Attribution for Explaining Representations. Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee |
| 2023 | Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions. Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison |
| 2023 | Contrastive Learning for Unsupervised Domain Adaptation of Time Series. Yilmazcan Özyurt, Stefan Feuerriegel, Ce Zhang |
| 2023 | Contrastive Meta-Learning for Partially Observable Few-Shot Learning. Adam Jelley, Amos J. Storkey, Antreas Antoniou, Sam Devlin |
| 2023 | Copy is All You Need. Tian Lan, Deng Cai, Yan Wang, Heyan Huang, Xian-Ling Mao |
| 2023 | Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation. Bariscan Bozkurt, Ates Isfendiyaroglu, Cengiz Pehlevan, Alper Tunga Erdogan |
| 2023 | Corrupted Image Modeling for Self-Supervised Visual Pre-Training. Yuxin Fang, Li Dong, Hangbo Bao, Xinggang Wang, Furu Wei |
| 2023 | Coupled Multiwavelet Operator Learning for Coupled Differential Equations. Xiongye Xiao, Defu Cao, Ruochen Yang, Gaurav Gupta, Gengshuo Liu, Chenzhong Yin, Radu Balan, Paul Bogdan |
| 2023 | Coverage-centric Coreset Selection for High Pruning Rates. Haizhong Zheng, Rui Liu, Fan Lai, Atul Prakash |
| 2023 | CrAM: A Compression-Aware Minimizer. Alexandra Peste, Adrian Vladu, Eldar Kurtic, Christoph H. Lampert, Dan Alistarh |
| 2023 | Critic Sequential Monte Carlo. Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba, Yunpeng Liu, Berend Zwartsenberg, Setareh Dabiri, Frank Wood, Adam Scibior |
| 2023 | Cross-Layer Retrospective Retrieving via Layer Attention. Yanwen Fang, Yuxi Cai, Jintai Chen, Jingyu Zhao, Guangjian Tian, Guodong Li |
| 2023 | Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification. Hao Zheng, Runqi Wang, Jianzhuang Liu, Asako Kanezaki |
| 2023 | Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting. Yunhao Zhang, Junchi Yan |
| 2023 | Curriculum-based Co-design of Morphology and Control of Voxel-based Soft Robots. Yuxing Wang, Shuang Wu, Haobo Fu, Qiang Fu, Tiantian Zhang, Yongzhe Chang, Xueqian Wang |
| 2023 | Cycle to Clique (Cy2C) Graph Neural Network: A Sight to See beyond Neighborhood Aggregation. Yun Young Choi, Sun Woo Park, Youngho Woo, U Jin Choi |
| 2023 | Cycle-consistent Masked AutoEncoder for Unsupervised Domain Generalization. Haiyang Yang, Xiaotong Li, Shixiang Tang, Feng Zhu, Yizhou Wang, Meilin Chen, Lei Bai, Rui Zhao, Wanli Ouyang |
| 2023 | D4AM: A General Denoising Framework for Downstream Acoustic Models. Chi-Chang Lee, Yu Tsao, Hsin-Min Wang, Chu-Song Chen |
| 2023 | D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory. Tianbo Li, Min Lin, Zheyuan Hu, Kunhao Zheng, Giovanni Vignale, Kenji Kawaguchi, A. H. Castro Neto, Kostya S. Novoselov, Shuicheng Yan |
| 2023 | DAG Learning on the Permutahedron. Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae |
| 2023 | DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks. Wenqian Li, Yinchuan Li, Zhigang Li, Jianye Hao, Yan Pang |
| 2023 | DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. Alexander Tyurin, Peter Richtárik |
| 2023 | DAVA: Disentangling Adversarial Variational Autoencoder. Benjamin Estermann, Roger Wattenhofer |
| 2023 | DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection. Jinrong Yang, Lin Song, Songtao Liu, Weixin Mao, Zeming Li, Xiaoping Li, Hongbin Sun, Jian Sun, Nanning Zheng |
| 2023 | DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf |
| 2023 | DDM Tiange Xiang, Mahmut Yurt, Ali B. Syed, Kawin Setsompop, Akshay Chaudhari |
| 2023 | DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems. Pierre Schumacher, Daniel F. B. Haeufle, Dieter Büchler, Syn Schmitt, Georg Martius |
| 2023 | DFPC: Data flow driven pruning of coupled channels without data. Tanay Narshana, Chaitanya Murti, Chiranjib Bhattacharyya |
| 2023 | DFlow: Learning to Synthesize Better Optical Flow Datasets via a Differentiable Pipeline. Byung-Ki Kwon, Nam Hyeon-Woo, Ji-Yun Kim, Tae-Hyun Oh |
| 2023 | DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan |
| 2023 | DINO as a von Mises-Fisher mixture model. Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten |
| 2023 | DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heung-Yeung Shum |
| 2023 | DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images. Bing Wang, Lu Chen, Bo Yang |
| 2023 | DamoFD: Digging into Backbone Design on Face Detection. Yang Liu, Jiankang Deng, Fei Wang, Lei Shang, Xuansong Xie, Baigui Sun |
| 2023 | Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer. Eric Qu, Xufang Luo, Dongsheng Li |
| 2023 | Data Valuation Without Training of a Model. Nohyun Ki, Hoyong Choi, Hye Won Chung |
| 2023 | Data augmentation alone can improve adversarial training. Lin Li, Michael W. Spratling |
| 2023 | Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity. Clare Elizabeth Heinbaugh, Emilio Luz-Ricca, Huajie Shao |
| 2023 | Dataless Knowledge Fusion by Merging Weights of Language Models. Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, Pengxiang Cheng |
| 2023 | Dataset Pruning: Reducing Training Data by Examining Generalization Influence. Shuo Yang, Zeke Xie, Hanyu Peng, Min Xu, Mingming Sun, Ping Li |
| 2023 | DaxBench: Benchmarking Deformable Object Manipulation with Differentiable Physics. Siwei Chen, Yiqing Xu, Cunjun Yu, Linfeng Li, Xiao Ma, Zhongwen Xu, David Hsu |
| 2023 | De Novo Molecular Generation via Connection-aware Motif Mining. Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, Tie-Yan Liu |
| 2023 | DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing. Pengcheng He, Jianfeng Gao, Weizhu Chen |
| 2023 | DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training. Wei Li, Linchao Zhu, Longyin Wen, Yi Yang |
| 2023 | DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases. Donghan Yu, Sheng Zhang, Patrick Ng, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Yiqun Hu, William Yang Wang, Zhiguo Wang, Bing Xiang |
| 2023 | Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games. Wenhao Zhan, Jason D. Lee, Zhuoran Yang |
| 2023 | Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. Liam H. Fowl, Jonas Geiping, Steven Reich, Yuxin Wen, Wojciech Czaja, Micah Goldblum, Tom Goldstein |
| 2023 | Decision S4: Efficient Sequence-Based RL via State Spaces Layers. Shmuel Bar-David, Itamar Zimerman, Eliya Nachmani, Lior Wolf |
| 2023 | Decision Transformer under Random Frame Dropping. Kaizhe Hu, Ray Chen Zheng, Yang Gao, Huazhe Xu |
| 2023 | Decompose to Generalize: Species-Generalized Animal Pose Estimation. Guangrui Li, Yifan Sun, Zongxin Yang, Yi Yang |
| 2023 | Decomposed Prompting: A Modular Approach for Solving Complex Tasks. Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish Sabharwal |
| 2023 | Decompositional Generation Process for Instance-Dependent Partial Label Learning. Congyu Qiao, Ning Xu, Xin Geng |
| 2023 | Deconstructing Distributions: A Pointwise Framework of Learning. Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, Preetum Nakkiran |
| 2023 | Decoupled Training for Long-Tailed Classification With Stochastic Representations. Giung Nam, Sunguk Jang, Juho Lee |
| 2023 | Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths. Ming Xu, Sourav Garg, Michael Milford, Stephen Gould |
| 2023 | Deep Ensembles for Graphs with Higher-order Dependencies. Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla |
| 2023 | Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models. Yong Zhong, Hongtao Liu, Xiaodong Liu, Fan Bao, Weiran Shen, Chongxuan Li |
| 2023 | Deep Generative Symbolic Regression. Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar |
| 2023 | Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization. Shahana Ibrahim, Tri Nguyen, Xiao Fu |
| 2023 | Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? Kaiqi Zhang, Yu-Xiang Wang |
| 2023 | Deep Learning on Implicit Neural Representations of Shapes. Luca De Luigi, Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano |
| 2023 | Deep Ranking Ensembles for Hyperparameter Optimization. Abdus Salam Khazi, Sebastian Pineda-Arango, Josif Grabocka |
| 2023 | Deep Reinforcement Learning for Cost-Effective Medical Diagnosis. Zheng Yu, Yikuan Li, Joseph C. Kim, Kaixuan Huang, Yuan Luo, Mengdi Wang |
| 2023 | Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation. Bobby He, James Martens, Guodong Zhang, Aleksandar Botev, Andrew Brock, Samuel L. Smith, Yee Whye Teh |
| 2023 | Deep Variational Implicit Processes. Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato |
| 2023 | Defending against Adversarial Audio via Diffusion Model. Shutong Wu, Jiongxiao Wang, Wei Ping, Weili Nie, Chaowei Xiao |
| 2023 | Deja Vu: Continual Model Generalization for Unseen Domains. Chenxi Liu, Lixu Wang, Lingjuan Lyu, Chen Sun, Xiao Wang, Qi Zhu |
| 2023 | Delta: Degradation-Free Fully Test-Time Adaptation. Bowen Zhao, Chen Chen, Shu-Tao Xia |
| 2023 | Delving into Semantic Scale Imbalance. Yanbiao Ma, Licheng Jiao, Fang Liu, Yuxin Li, Shuyuan Yang, Xu Liu |
| 2023 | Denoising Diffusion Error Correction Codes. Yoni Choukroun, Lior Wolf |
| 2023 | Denoising Diffusion Samplers. Francisco Vargas, Will Sussman Grathwohl, Arnaud Doucet |
| 2023 | Denoising Masked Autoencoders Help Robust Classification. Quanlin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He |
| 2023 | Dense RGB Slam with Neural Implicit Maps. Heng Li, Xiaodong Gu, Weihao Yuan, Luwei Yang, Zilong Dong, Ping Tan |
| 2023 | DensePure: Understanding Diffusion Models for Adversarial Robustness. Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song |
| 2023 | Depth Separation with Multilayer Mean-Field Networks. Yunwei Ren, Mo Zhou, Rong Ge |
| 2023 | DepthFL : Depthwise Federated Learning for Heterogeneous Clients. Minjae Kim, Sangyoon Yu, Suhyun Kim, Soo-Mook Moon |
| 2023 | Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling. Keyu Tian, Yi Jiang, Qishuai Diao, Chen Lin, Liwei Wang, Zehuan Yuan |
| 2023 | Deterministic training of generative autoencoders using invertible layers. Gianluigi Silvestri, Daan Roos, Luca Ambrogioni |
| 2023 | DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics. Sizhe Li, Zhiao Huang, Tao Chen, Tao Du, Hao Su, Joshua B. Tenenbaum, Chuang Gan |
| 2023 | DiGress: Discrete Denoising diffusion for graph generation. Clément Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, Pascal Frossard |
| 2023 | Diagnosing and Rectifying Vision Models using Language. Yuhui Zhang, Jeff Z. HaoChen, Shih-Cheng Huang, Kuan-Chieh Wang, James Zou, Serena Yeung |
| 2023 | Dichotomy of Control: Separating What You Can Control from What You Cannot. Sherry Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum |
| 2023 | DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola |
| 2023 | DiffEdit: Diffusion-based semantic image editing with mask guidance. Guillaume Couairon, Jakob Verbeek, Holger Schwenk, Matthieu Cord |
| 2023 | DiffMimic: Efficient Motion Mimicking with Differentiable Physics. Jiawei Ren, Cunjun Yu, Siwei Chen, Xiao Ma, Liang Pan, Ziwei Liu |
| 2023 | Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models. Dongzhuo Li |
| 2023 | Differentiable Mathematical Programming for Object-Centric Representation Learning. Adeel Pervez, Phillip Lippe, Efstratios Gavves |
| 2023 | Differentially Private $L_2$-Heavy Hitters in the Sliding Window Model. Jeremiah Blocki, Seunghoon Lee, Tamalika Mukherjee, Samson Zhou |
| 2023 | Differentially Private Adaptive Optimization with Delayed Preconditioners. Tian Li, Manzil Zaheer, Ken Liu, Sashank J. Reddi, Hugh Brendan McMahan, Virginia Smith |
| 2023 | DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, Lingpeng Kong |
| 2023 | DiffusER: Diffusion via Edit-based Reconstruction. Machel Reid, Vincent Josua Hellendoorn, Graham Neubig |
| 2023 | Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation. Boah Kim, Yujin Oh, Jong Chul Ye |
| 2023 | Diffusion Models Already Have A Semantic Latent Space. Mingi Kwon, Jaeseok Jeong, Youngjung Uh |
| 2023 | Diffusion Models for Causal Discovery via Topological Ordering. Pedro Sanchez, Xiao Liu, Alison Q. O'Neil, Sotirios A. Tsaftaris |
| 2023 | Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning. Zhendong Wang, Jonathan J. Hunt, Mingyuan Zhou |
| 2023 | Diffusion Posterior Sampling for General Noisy Inverse Problems. Hyungjin Chung, Jeongsol Kim, Michael Thompson McCann, Marc Louis Klasky, Jong Chul Ye |
| 2023 | Diffusion Probabilistic Fields. Peiye Zhuang, Samira Abnar, Jiatao Gu, Alexander G. Schwing, Joshua M. Susskind, Miguel Ángel Bautista |
| 2023 | Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem. Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola |
| 2023 | Diffusion-GAN: Training GANs with Diffusion. Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou |
| 2023 | Diffusion-based Image Translation using disentangled style and content representation. Gihyun Kwon, Jong Chul Ye |
| 2023 | Dilated convolution with learnable spacings. Ismail Khalfaoui Hassani, Thomas Pellegrini, Timothée Masquelier |
| 2023 | Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning. Daniel Palenicek, Michael Lutter, Joao Carvalho, Jan Peters |
| 2023 | Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs. Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim, Tong Yu, Jane Hoffswell, Nedim Lipka, Shunan Guo, Cameron Musco |
| 2023 | Dirichlet-based Uncertainty Calibration for Active Domain Adaptation. Mixue Xie, Shuang Li, Rui Zhang, Chi Harold Liu |
| 2023 | Discovering Evolution Strategies via Meta-Black-Box Optimization. Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag |
| 2023 | Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data. Fuxiang Zhang, Chengxing Jia, Yi-Chen Li, Lei Yuan, Yang Yu, Zongzhang Zhang |
| 2023 | Discovering Informative and Robust Positives for Video Domain Adaptation. Chang Liu, Kunpeng Li, Michael Stopa, Jun Amano, Yun Fu |
| 2023 | Discovering Latent Knowledge in Language Models Without Supervision. Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt |
| 2023 | Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality. Tom Zahavy, Yannick Schroecker, Feryal M. P. Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh |
| 2023 | Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation. Ye Zhu, Yu Wu, Kyle Olszewski, Jian Ren, Sergey Tulyakov, Yan Yan |
| 2023 | Discrete Predictor-Corrector Diffusion Models for Image Synthesis. José Lezama, Tim Salimans, Lu Jiang, Huiwen Chang, Jonathan Ho, Irfan Essa |
| 2023 | Disentanglement of Correlated Factors via Hausdorff Factorized Support. Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, Diane Bouchacourt |
| 2023 | Disentanglement with Biological Constraints: A Theory of Functional Cell Types. James C. R. Whittington, Will Dorrell, Surya Ganguli, Timothy Behrens |
| 2023 | Disentangling Learning Representations with Density Estimation. Eric C. Yeats, Frank Y. Liu, Hai Helen Li |
| 2023 | Disentangling the Mechanisms Behind Implicit Regularization in SGD. Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary Chase Lipton |
| 2023 | Disparate Impact in Differential Privacy from Gradient Misalignment. Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, Jesse C. Cresswell |
| 2023 | Distilling Cognitive Backdoor Patterns within an Image. Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey |
| 2023 | Distilling Model Failures as Directions in Latent Space. Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry |
| 2023 | Distributed Differential Privacy in Multi-Armed Bandits. Sayak Ray Chowdhury, Xingyu Zhou |
| 2023 | Distributed Extra-gradient with Optimal Complexity and Communication Guarantees. Ali Ramezani-Kebrya, Kimon Antonakopoulos, Igor Krawczuk, Justin Deschenaux, Volkan Cevher |
| 2023 | Distributional Meta-Gradient Reinforcement Learning. Haiyan Yin, Shuicheng Yan, Zhongwen Xu |
| 2023 | Distributionally Robust Post-hoc Classifiers under Prior Shifts. Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid, Wen-Sheng Chu, Yang Liu, Abhishek Kumar |
| 2023 | Distributionally Robust Recourse Action. Duy Nguyen, Ngoc Bui, Viet Anh Nguyen |
| 2023 | Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement. Yoonho Lee, Huaxiu Yao, Chelsea Finn |
| 2023 | Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors. Jianfei Yang, Xiangyu Peng, Kai Wang, Zheng Zhu, Jiashi Feng, Lihua Xie, Yang You |
| 2023 | Do We Really Need Complicated Model Architectures For Temporal Networks? Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, Mehrdad Mahdavi |
| 2023 | DocPrompting: Generating Code by Retrieving the Docs. Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao Jiang, Graham Neubig |
| 2023 | Does Deep Learning Learn to Abstract? A Systematic Probing Framework. Shengnan An, Zeqi Lin, Bei Chen, Qiang Fu, Nanning Zheng, Jian-Guang Lou |
| 2023 | Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision? Lirui Wang, Kaiqing Zhang, Yunzhu Li, Yonglong Tian, Russ Tedrake |
| 2023 | Does Zero-Shot Reinforcement Learning Exist? Ahmed Touati, Jérémy Rapin, Yann Ollivier |
| 2023 | Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach. Minyoung Kim, Da Li, Timothy M. Hospedales |
| 2023 | Domain Generalization via Heckman-type Selection Models. Hyungu Kahng, Hyungrok Do, Judy Zhong |
| 2023 | Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation. Zihao Xu, Guang-Yuan Hao, Hao He, Hao Wang |
| 2023 | Don't fear the unlabelled: safe semi-supervised learning via debiasing. Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei |
| 2023 | Don't forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure. Daksh Idnani, Vivek Madan, Naman Goyal, David J. Schwab, Ramakrishna Vedantam |
| 2023 | Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness. Shuaichen Chang, Jun Wang, Mingwen Dong, Lin Pan, Henghui Zhu, Alexander Hanbo Li, Wuwei Lan, Sheng Zhang, Jiarong Jiang, Joseph Lilien, Steve Ash, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Bing Xiang |
| 2023 | Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs. Albert Qiaochu Jiang, Sean Welleck, Jin Peng Zhou, Timothée Lacroix, Jiacheng Liu, Wenda Li, Mateja Jamnik, Guillaume Lample, Yuhuai Wu |
| 2023 | DreamFusion: Text-to-3D using 2D Diffusion. Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall |
| 2023 | DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training. Joya Chen, Kai Xu, Yuhui Wang, Yifei Cheng, Angela Yao |
| 2023 | Dual Algorithmic Reasoning. Danilo Numeroso, Davide Bacciu, Petar Velickovic |
| 2023 | Dual Diffusion Implicit Bridges for Image-to-Image Translation. Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon |
| 2023 | Dual Student Networks for Data-Free Model Stealing. James Beetham, Navid Kardan, Ajmal Saeed Mian, Mubarak Shah |
| 2023 | DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation. Yan Zhao, Ruihai Wu, Zhehuan Chen, Yourong Zhang, Qingnan Fan, Kaichun Mo, Hao Dong |
| 2023 | DySR: Adaptive Super-Resolution via Algorithm and System Co-design. Syed Zawad, Cheng Li, Zhewei Yao, Elton Zheng, Yuxiong He, Feng Yan |
| 2023 | DynaMS: Dyanmic Margin Selection for Efficient Deep Learning. Jiaxing Wang, Yong Li, Jingwei Zhuo, Xupeng Shi, Weizhong Zhang, Lixing Gong, Tong Tao, Pengzhang Liu, Yongjun Bao, Weipeng Yan |
| 2023 | Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning. Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan |
| 2023 | Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting. Nicolai Dorka, Tim Welschehold, Wolfram Burgard |
| 2023 | E-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation. Jie Zhu, Huabin Huang, Banghuai Li, Leye Wang |
| 2023 | E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking. Yangtian Zhang, Huiyu Cai, Chence Shi, Jian Tang |
| 2023 | EA-HAS-Bench: Energy-aware Hyperparameter and Architecture Search Benchmark. Shuguang Dou, Xinyang Jiang, Cairong Zhao, Dongsheng Li |
| 2023 | EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers. Steeven Janny, Aurélien Béneteau, Madiha Nadri, Julie Digne, Nicolas Thome, Christian Wolf |
| 2023 | EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. Michael Crawshaw, Yajie Bao, Mingrui Liu |
| 2023 | ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation. Jianye Hao, Pengyi Li, Hongyao Tang, Yan Zheng, Xian Fu, Zhaopeng Meng |
| 2023 | ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret. Stephen Marcus McAleer, Gabriele Farina, Marc Lanctot, Tuomas Sandholm |
| 2023 | ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure. Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon, Sunjae Yoon, Gwangsu Kim, Yingzhen Li, Chang D. Yoo |
| 2023 | EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model. Yifu Yuan, Jianye Hao, Fei Ni, Yao Mu, Yan Zheng, Yujing Hu, Jinyi Liu, Yingfeng Chen, Changjie Fan |
| 2023 | EVA3D: Compositional 3D Human Generation from 2D Image Collections. Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan, Ziwei Liu |
| 2023 | EVC: Towards Real-Time Neural Image Compression with Mask Decay. Guo-Hua Wang, Jiahao Li, Bin Li, Yan Lu |
| 2023 | Easy Differentially Private Linear Regression. Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii |
| 2023 | Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis. Hao Tang, Xiaojuan Qi, Guolei Sun, Dan Xu, Nicu Sebe, Radu Timofte, Luc Van Gool |
| 2023 | Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks. Bowen Jin, Yu Zhang, Yu Meng, Jiawei Han |
| 2023 | Editing models with task arithmetic. Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi |
| 2023 | Effective Self-supervised Pre-training on Low-compute Networks without Distillation. Fuwen Tan, Fatemeh Sadat Saleh, Brais Martínez |
| 2023 | Effective passive membership inference attacks in federated learning against overparameterized models. Jiacheng Li, Ninghui Li, Bruno Ribeiro |
| 2023 | Effectively Modeling Time Series with Simple Discrete State Spaces. Michael Zhang, Khaled Kamal Saab, Michael Poli, Tri Dao, Karan Goel, Christopher Ré |
| 2023 | Effects of Graph Convolutions in Multi-layer Networks. Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath |
| 2023 | Efficient Attention via Control Variates. Lin Zheng, Jianbo Yuan, Chong Wang, Lingpeng Kong |
| 2023 | Efficient Certified Training and Robustness Verification of Neural ODEs. Mustafa Zeqiri, Mark Niklas Müller, Marc Fischer, Martin T. Vechev |
| 2023 | Efficient Conditionally Invariant Representation Learning. Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton |
| 2023 | Efficient Deep Reinforcement Learning Requires Regulating Overfitting. Qiyang Li, Aviral Kumar, Ilya Kostrikov, Sergey Levine |
| 2023 | Efficient Discrete Multi Marginal Optimal Transport Regularization. Ronak Mehta, Jeffery Kline, Vishnu Suresh Lokhande, Glenn Fung, Vikas Singh |
| 2023 | Efficient Edge Inference by Selective Query. Anil Kag, Igor Fedorov, Aditya Gangrade, Paul N. Whatmough, Venkatesh Saligrama |
| 2023 | Efficient Federated Domain Translation. Zeyu Zhou, Sheikh Shams Azam, Christopher G. Brinton, David I. Inouye |
| 2023 | Efficient Model Updates for Approximate Unlearning of Graph-Structured Data. Eli Chien, Chao Pan, Olgica Milenkovic |
| 2023 | Efficient Offline Policy Optimization with a Learned Model. Zichen Liu, Siyi Li, Wee Sun Lee, Shuicheng Yan, Zhongwen Xu |
| 2023 | Efficient Planning in a Compact Latent Action Space. Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian |
| 2023 | Efficient approximation of neural population structure and correlations with probabilistic circuits. Koosha Khalvati, Samantha Johnson, Stefan Mihalas, Michael A. Buice |
| 2023 | Efficient recurrent architectures through activity sparsity and sparse back-propagation through time. Anand Subramoney, Khaleelulla Khan Nazeer, Mark Schöne, Christian Mayr, David Kappel |
| 2023 | Efficiently Computing Nash Equilibria in Adversarial Team Markov Games. Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas, Emmanouil V. Vlatakis-Gkaragkounis, Vaggos Chatziafratis, Stelios Andrew Stavroulakis |
| 2023 | Efficiently Controlling Multiple Risks with Pareto Testing. Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay, Tommi S. Jaakkola |
| 2023 | Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement. Chongyi Li, Chun-Le Guo, Man Zhou, Zhexin Liang, Shangchen Zhou, Ruicheng Feng, Chen Change Loy |
| 2023 | Emergence of Maps in the Memories of Blind Navigation Agents. Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos, Dhruv Batra |
| 2023 | Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task. Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda B. Viégas, Hanspeter Pfister, Martin Wattenberg |
| 2023 | Empowering Graph Representation Learning with Test-Time Graph Transformation. Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah |
| 2023 | Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution. Zikai Sun, Thierry Blu |
| 2023 | Encoding Recurrence into Transformers. Feiqing Huang, Kexin Lu, Yuxi Cai, Zhen Qin, Yanwen Fang, Guangjian Tian, Guodong Li |
| 2023 | Energy-Based Test Sample Adaptation for Domain Generalization. Zehao Xiao, Xiantong Zhen, Shengcai Liao, Cees G. M. Snoek |
| 2023 | Energy-Inspired Self-Supervised Pretraining for Vision Models. Ze Wang, Jiang Wang, Zicheng Liu, Qiang Qiu |
| 2023 | Energy-based Out-of-Distribution Detection for Graph Neural Networks. Qitian Wu, Yiting Chen, Chenxiao Yang, Junchi Yan |
| 2023 | Enhancing Meta Learning via Multi-Objective Soft Improvement Functions. Runsheng Yu, Weiyu Chen, Xinrun Wang, James T. Kwok |
| 2023 | Enhancing the Inductive Biases of Graph Neural ODE for Modeling Physical Systems. Suresh Bishnoi, Ravinder Bhattoo, Jayadeva, Sayan Ranu, N. M. Anoop Krishnan |
| 2023 | Ensuring DNN Solution Feasibility for Optimization Problems with Linear Constraints. Tianyu Zhao, Xiang Pan, Minghua Chen, Steven H. Low |
| 2023 | Equal Improvability: A New Fairness Notion Considering the Long-term Impact. Ozgur Guldogan, Yuchen Zeng, Jy-yong Sohn, Ramtin Pedarsani, Kangwook Lee |
| 2023 | EquiMod: An Equivariance Module to Improve Visual Instance Discrimination. Alexandre Devillers, Mathieu Lefort |
| 2023 | Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. Yi-Lun Liao, Tess E. Smidt |
| 2023 | Equivariance-aware Architectural Optimization of Neural Networks. Kaitlin Maile, Dennis George Wilson, Patrick Forré |
| 2023 | Equivariant Descriptor Fields: SE(3)-Equivariant Energy-Based Models for End-to-End Visual Robotic Manipulation Learning. Hyunwoo Ryu, Hong-in Lee, Jeong-Hoon Lee, Jongeun Choi |
| 2023 | Equivariant Energy-Guided SDE for Inverse Molecular Design. Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu |
| 2023 | Equivariant Hypergraph Diffusion Neural Operators. Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li |
| 2023 | Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design. Keir Adams, Connor W. Coley |
| 2023 | Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning. Fahad Sarfraz, Elahe Arani, Bahram Zonooz |
| 2023 | Estimating individual treatment effects under unobserved confounding using binary instruments. Dennis Frauen, Stefan Feuerriegel |
| 2023 | Eva: Practical Second-order Optimization with Kronecker-vectorized Approximation. Lin Zhang, Shaohuai Shi, Bo Li |
| 2023 | Evaluating Long-Term Memory in 3D Mazes. Jurgis Pasukonis, Timothy P. Lillicrap, Danijar Hafner |
| 2023 | Evaluating Representations with Readout Model Switching. Yazhe Li, Jörg Bornschein, Marcus Hutter |
| 2023 | Everybody Needs Good Neighbours: An Unsupervised Locality-based Method for Bias Mitigation. Xudong Han, Timothy Baldwin, Trevor Cohn |
| 2023 | Evidential Uncertainty and Diversity Guided Active Learning for Scene Graph Generation. Shuzhou Sun, Shuaifeng Zhi, Janne Heikkilä, Li Liu |
| 2023 | Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems. Zhong Yi Wan, Leonardo Zepeda-Núñez, Anudhyan Boral, Fei Sha |
| 2023 | Evolving Populations of Diverse RL Agents with MAP-Elites. Thomas Pierrot, Arthur Flajolet |
| 2023 | Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods. Shunta Akiyama, Taiji Suzuki |
| 2023 | Explaining RL Decisions with Trajectories. Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal, Georgios Theocharous, Jayakumar Subramanian |
| 2023 | Explaining Temporal Graph Models through an Explorer-Navigator Framework. Wenwen Xia, Mincai Lai, Caihua Shan, Yao Zhang, Xinnan Dai, Xiang Li, Dongsheng Li |
| 2023 | Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation. Jie Yang, Ailing Zeng, Shilong Liu, Feng Li, Ruimao Zhang, Lei Zhang |
| 2023 | Explicitly Minimizing the Blur Error of Variational Autoencoders. Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya, Ertunc Erdil, Ender Konukoglu |
| 2023 | Exploring Active 3D Object Detection from a Generalization Perspective. Yadan Luo, Zhuoxiao Chen, Zijian Wang, Xin Yu, Zi Huang, Mahsa Baktashmotlagh |
| 2023 | Exploring Low-Rank Property in Multiple Instance Learning for Whole Slide Image Classification. Jinxi Xiang, Jun Zhang |
| 2023 | Exploring Temporally Dynamic Data Augmentation for Video Recognition. Taeoh Kim, Jinhyung Kim, Minho Shim, Sangdoo Yun, Myunggu Kang, Dongyoon Wee, Sangyoun Lee |
| 2023 | Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders. Youngwan Lee, Jeffrey Ryan Willette, Jonghee Kim, Juho Lee, Sung Ju Hwang |
| 2023 | Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness. Yuancheng Xu, Yanchao Sun, Micah Goldblum, Tom Goldstein, Furong Huang |
| 2023 | Exploring perceptual straightness in learned visual representations. Anne Harrington, Vasha DuTell, Ayush Tewari, Mark Hamilton, Simon Stent, Ruth Rosenholtz, William T. Freeman |
| 2023 | Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping. Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian |
| 2023 | Exponential Generalization Bounds with Near-Optimal Rates for $L_q$-Stable Algorithms. Xiaotong Yuan, Ping Li |
| 2023 | ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion. Aleksandar Pavlovic, Emanuel Sallinger |
| 2023 | Expressive Monotonic Neural Networks. Niklas Nolte, Ouail Kitouni, Mike Williams |
| 2023 | Extracting Robust Models with Uncertain Examples. Guanlin Li, Guowen Xu, Shangwei Guo, Han Qiu, Jiwei Li, Tianwei Zhang |
| 2023 | Extreme Q-Learning: MaxEnt RL without Entropy. Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon |
| 2023 | Extremely Simple Activation Shaping for Out-of-Distribution Detection. Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, Rosanne Liu |
| 2023 | FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data. Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J. Su, James Zou |
| 2023 | FIGARO: Controllable Music Generation using Learned and Expert Features. Dimitri von Rütte, Luca Biggio, Yannic Kilcher, Thomas Hofmann |
| 2023 | FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities. Takashi Matsubara, Takaharu Yaguchi |
| 2023 | FIT: A Metric for Model Sensitivity. Ben Zandonati, Adrian Alan Pol, Maurizio Pierini, Olya Sirkin, Tal Kopetz |
| 2023 | FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning. Kaiyuan Zhang, Guanhong Tao, Qiuling Xu, Siyuan Cheng, Shengwei An, Yingqi Liu, Shiwei Feng, Guangyu Shen, Pin-Yu Chen, Shiqing Ma, Xiangyu Zhang |
| 2023 | Factorized Fourier Neural Operators. Alasdair Tran, Alexander Patrick Mathews, Lexing Xie, Cheng Soon Ong |
| 2023 | FaiREE: fair classification with finite-sample and distribution-free guarantee. Puheng Li, James Zou, Linjun Zhang |
| 2023 | Fair Attribute Completion on Graph with Missing Attributes. Dongliang Guo, Zhixuan Chu, Sheng Li |
| 2023 | FairGBM: Gradient Boosting with Fairness Constraints. André Ferreira Cruz, Catarina G. Belém, João Bravo, Pedro Saleiro, Pedro Bizarro |
| 2023 | Fairness and Accuracy under Domain Generalization. Thai-Hoang Pham, Xueru Zhang, Ping Zhang |
| 2023 | Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes. Fengda Zhang, Kun Kuang, Long Chen, Yuxuan Liu, Chao Wu, Jun Xiao |
| 2023 | Fake It Until You Make It : Towards Accurate Near-Distribution Novelty Detection. Hossein Mirzaei, Mohammadreza Salehi, Sajjad Shahabi, Efstratios Gavves, Cees G. M. Snoek, Mohammad Sabokrou, Mohammad Hossein Rohban |
| 2023 | Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems. Yihao Feng, Shentao Yang, Shujian Zhang, Jianguo Zhang, Caiming Xiong, Mingyuan Zhou, Huan Wang |
| 2023 | Fast Nonlinear Vector Quantile Regression. Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano, Alexander M. Bronstein |
| 2023 | Fast Sampling of Diffusion Models with Exponential Integrator. Qinsheng Zhang, Yongxin Chen |
| 2023 | Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search. Michal Zawalski, Michal Tyrolski, Konrad Czechowski, Tomasz Odrzygózdz, Damian Stachura, Piotr Piekos, Yuhuai Wu, Lukasz Kucinski, Piotr Milos |
| 2023 | FastFill: Efficient Compatible Model Update. Florian Jaeckle, Fartash Faghri, Ali Farhadi, Oncel Tuzel, Hadi Pouransari |
| 2023 | Faster Gradient-Free Methods for Escaping Saddle Points. Hualin Zhang, Bin Gu |
| 2023 | Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games. Shicong Cen, Yuejie Chi, Simon Shaolei Du, Lin Xiao |
| 2023 | Faster federated optimization under second-order similarity. Ahmed Khaled, Chi Jin |
| 2023 | Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks. Sravanti Addepalli, Anshul Nasery, Venkatesh Babu Radhakrishnan, Praneeth Netrapalli, Prateek Jain |
| 2023 | Feature selection and low test error in shallow low-rotation ReLU networks. Matus Telgarsky |
| 2023 | FedDAR: Federated Domain-Aware Representation Learning. Aoxiao Zhong, Hao He, Zhaolin Ren, Na Li, Quanzheng Li |
| 2023 | FedExP: Speeding Up Federated Averaging via Extrapolation. Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi |
| 2023 | FedFA: Federated Feature Augmentation. Tianfei Zhou, Ender Konukoglu |
| 2023 | FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy. Yan Sun, Li Shen, Tiansheng Huang, Liang Ding, Dacheng Tao |
| 2023 | Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing |
| 2023 | Federated Learning from Small Datasets. Michael Kamp, Jonas Fischer, Jilles Vreeken |
| 2023 | Federated Nearest Neighbor Machine Translation. Yichao Du, Zhirui Zhang, Bingzhe Wu, Lemao Liu, Tong Xu, Enhong Chen |
| 2023 | Federated Neural Bandits. Zhongxiang Dai, Yao Shu, Arun Verma, Flint Xiaofeng Fan, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2023 | Few-Shot Domain Adaptation For End-to-End Communication. Jayaram Raghuram, Yijing Zeng, Dolores García, Rafael Ruiz, Somesh Jha, Joerg Widmer, Suman Banerjee |
| 2023 | Few-shot Backdoor Attacks via Neural Tangent Kernels. Jonathan Hayase, Sewoong Oh |
| 2023 | Few-shot Cross-domain Image Generation via Inference-time Latent-code Learning. Arnab Kumar Mondal, Piyush Tiwary, Parag Singla, Prathosh AP |
| 2023 | FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, Richard E. Turner |
| 2023 | Filter-Recovery Network for Multi-Speaker Audio-Visual Speech Separation. Haoyue Cheng, Zhaoyang Liu, Wayne Wu, Limin Wang |
| 2023 | Finding Actual Descent Directions for Adversarial Training. Fabian Latorre, Igor Krawczuk, Leello Tadesse Dadi, Thomas Pethick, Volkan Cevher |
| 2023 | Finding the Global Semantic Representation in GAN through Fréchet Mean. Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, Myungjoo Kang |
| 2023 | First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains. Kefan Dong, Tengyu Ma |
| 2023 | Fisher-Legendre (FishLeg) optimization of deep neural networks. Jezabel R. Garcia, Federica Freddi, Stathi Fotiadis, Maolin Li, Sattar Vakili, Alberto Bernacchia, Guillaume Hennequin |
| 2023 | Flow Annealed Importance Sampling Bootstrap. Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato |
| 2023 | Flow Matching for Generative Modeling. Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le |
| 2023 | Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. Xingchao Liu, Chengyue Gong, Qiang Liu |
| 2023 | FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation. Zhou Xian, Bo Zhu, Zhenjia Xu, Hsiao-Yu Tung, Antonio Torralba, Katerina Fragkiadaki, Chuang Gan |
| 2023 | FoSR: First-order spectral rewiring for addressing oversquashing in GNNs. Kedar Karhadkar, Pradeep Kr. Banerjee, Guido Montúfar |
| 2023 | Fooling SHAP with Stealthily Biased Sampling. Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh |
| 2023 | Formal Mathematics Statement Curriculum Learning. Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever |
| 2023 | Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions. Zeyuan Allen-Zhu, Yuanzhi Li |
| 2023 | Free Lunch for Domain Adversarial Training: Environment Label Smoothing. Yifan Zhang, Xue Wang, Jian Liang, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan |
| 2023 | FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning. Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu, Jindong Wang, Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt Schiele, Xing Xie |
| 2023 | From $t$-SNE to UMAP with contrastive learning. Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak |
| 2023 | From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data. Zichen Jeff Cui, Yibin Wang, Nur Muhammad (Mahi) Shafiullah, Lerrel Pinto |
| 2023 | Function-Consistent Feature Distillation. Dongyang Liu, Meina Kan, Shiguang Shan, Xilin Chen |
| 2023 | Function-space regularized Rényi divergences. Jeremiah Birrell, Yannis Pantazis, Paul Dupuis, Luc Rey-Bellet, Markos A. Katsoulakis |
| 2023 | Fundamental Limits in Formal Verification of Message-Passing Neural Networks. Marco Sälzer, Martin Lange |
| 2023 | Fundamental limits on the robustness of image classifiers. Zheng Dai, David Gifford |
| 2023 | FunkNN: Neural Interpolation for Functional Generation. AmirEhsan Khorashadizadeh, Anadi Chaman, Valentin Debarnot, Ivan Dokmanic |
| 2023 | Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation. Zhengrui Ma, Chenze Shao, Shangtong Gui, Min Zhang, Yang Feng |
| 2023 | GAIN: On the Generalization of Instructional Action Understanding. Junlong Li, Guangyi Chen, Yansong Tang, Jinan Bao, Kun Zhang, Jie Zhou, Jiwen Lu |
| 2023 | GAMR: A Guided Attention Model for (visual) Reasoning. Mohit Vaishnav, Thomas Serre |
| 2023 | GEASS: Neural causal feature selection for high-dimensional biological data. Mingze Dong, Yuval Kluger |
| 2023 | GFlowNets and variational inference. Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio |
| 2023 | GLM-130B: An Open Bilingual Pre-trained Model. Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, Jie Tang |
| 2023 | GNNDelete: A General Strategy for Unlearning in Graph Neural Networks. Jiali Cheng, George Dasoulas, Huan He, Chirag Agarwal, Marinka Zitnik |
| 2023 | GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks. Xiaoqi Wang, Han-Wei Shen |
| 2023 | GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure. Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets, Mihaela van der Schaar |
| 2023 | GOOD: Exploring geometric cues for detecting objects in an open world. Haiwen Huang, Andreas Geiger, Dan Zhang |
| 2023 | GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation. Chenhongyi Yang, Jiarui Xu, Shalini De Mello, Elliot J. Crowley, Xiaolong Wang |
| 2023 | GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints. Mohammadsajad Abavisani, David Danks, Sergey M. Plis |
| 2023 | GReTo: Remedying dynamic graph topology-task discordance via target homophily. Zhengyang Zhou, Qihe Huang, Gengyu Lin, Kuo Yang, Lei Bai, Yang Wang |
| 2023 | GeneFace: Generalized and High-Fidelity Audio-Driven 3D Talking Face Synthesis. Zhenhui Ye, Ziyue Jiang, Yi Ren, Jinglin Liu, Jinzheng He, Zhou Zhao |
| 2023 | General Neural Gauge Fields. Fangneng Zhan, Lingjie Liu, Adam Kortylewski, Christian Theobalt |
| 2023 | Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses. Xiaolin Hu, Shaojie Li, Yong Liu |
| 2023 | Generalization and Estimation Error Bounds for Model-based Neural Networks. Avner Shultzman, Eyar Azar, Miguel R. D. Rodrigues, Yonina C. Eldar |
| 2023 | Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time. Qingchun Hou, Jingwei Yang, Yiqiang Su, Xiaoqing Wang, Yuming Deng |
| 2023 | Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks. Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang |
| 2023 | Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap. Weiyang Liu, Longhui Yu, Adrian Weller, Bernhard Schölkopf |
| 2023 | Generate rather than Retrieve: Large Language Models are Strong Context Generators. Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, Meng Jiang |
| 2023 | Generating Diverse Cooperative Agents by Learning Incompatible Policies. Rujikorn Charakorn, Poramate Manoonpong, Nat Dilokthanakul |
| 2023 | Generating Sequences by Learning to Self-Correct. Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, Yejin Choi |
| 2023 | Generative Augmented Flow Networks. Ling Pan, Dinghuai Zhang, Aaron C. Courville, Longbo Huang, Yoshua Bengio |
| 2023 | Generative Modeling Helps Weak Supervision (and Vice Versa). Benedikt Boecking, Nicholas Carl Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski |
| 2023 | Generative Modelling with Inverse Heat Dissipation. Severi Rissanen, Markus Heinonen, Arno Solin |
| 2023 | Geometrically regularized autoencoders for non-Euclidean data. Cheongjae Jang, Yonghyeon Lee, Yung-Kyun Noh, Frank C. Park |
| 2023 | Git Re-Basin: Merging Models modulo Permutation Symmetries. Samuel K. Ainsworth, Jonathan Hayase, Siddhartha S. Srinivasa |
| 2023 | Global Explainability of GNNs via Logic Combination of Learned Concepts. Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Liò, Andrea Passerini |
| 2023 | Globally Optimal Training of Neural Networks with Threshold Activation Functions. Tolga Ergen, Halil Ibrahim Gulluk, Jonathan Lacotte, Mert Pilanci |
| 2023 | GoBigger: A Scalable Platform for Cooperative-Competitive Multi-Agent Interactive Simulation. Ming Zhang, Shenghan Zhang, Zhenjie Yang, Lekai Chen, Jinliang Zheng, Chao Yang, Chuming Li, Hang Zhou, Yazhe Niu, Yu Liu |
| 2023 | Gradient Boosting Performs Gaussian Process Inference. Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova |
| 2023 | Gradient Gating for Deep Multi-Rate Learning on Graphs. T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra |
| 2023 | Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models. Meng Liu, Haoran Liu, Shuiwang Ji |
| 2023 | Graph Contrastive Learning for Skeleton-based Action Recognition. Xiaohu Huang, Hao Zhou, Jian Wang, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang, Xinggang Wang, Wenyu Liu, Bin Feng |
| 2023 | Graph Domain Adaptation via Theory-Grounded Spectral Regularization. Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen |
| 2023 | Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning. Zehao Niu, Mihai Anitescu, Jie Chen |
| 2023 | Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs. Chenxiao Yang, Qitian Wu, Jiahua Wang, Junchi Yan |
| 2023 | Graph Neural Networks for Link Prediction with Subgraph Sketching. Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M. Bronstein, Max Hansmire |
| 2023 | Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution. Chao Chen, Haoyu Geng, Gang Zeng, Zhaobing Han, Hua Chai, Xiaokang Yang, Junchi Yan |
| 2023 | Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems. Zhongyuan Zhao, Ananthram Swami, Santiago Segarra |
| 2023 | Gray-Box Gaussian Processes for Automated Reinforcement Learning. Gresa Shala, André Biedenkapp, Frank Hutter, Josif Grabocka |
| 2023 | Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement. Samuel Neumann, Sungsu Lim, Ajin George Joseph, Yangchen Pan, Adam White, Martha White |
| 2023 | Gromov-Wasserstein Autoencoders. Nao Nakagawa, Ren Togo, Takahiro Ogawa, Miki Haseyama |
| 2023 | Grounding Graph Network Simulators using Physical Sensor Observations. Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl, Franziska Mathis-Ullrich, Gerhard Neumann |
| 2023 | Guarded Policy Optimization with Imperfect Online Demonstrations. Zhenghai Xue, Zhenghao Peng, Quanyi Li, Zhihan Liu, Bolei Zhou |
| 2023 | Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners. Seonghyeon Ye, Doyoung Kim, Joel Jang, Joongbo Shin, Minjoon Seo |
| 2023 | Guiding Energy-based Models via Contrastive Latent Variables. Hankook Lee, Jongheon Jeong, Sejun Park, Jinwoo Shin |
| 2023 | Guiding Safe Exploration with Weakest Preconditions. Greg Anderson, Swarat Chaudhuri, Isil Dillig |
| 2023 | Guiding continuous operator learning through Physics-based boundary constraints. Nadim Saad, Gaurav Gupta, Shima Alizadeh, Danielle C. Maddix |
| 2023 | H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection. Xue Yang, Gefan Zhang, Wentong Li, Yue Zhou, Xuehui Wang, Junchi Yan |
| 2023 | Hard-Meta-Dataset++: Towards Understanding Few-Shot Performance on Difficult Tasks. Samyadeep Basu, Megan Stanley, John Bronskill, Soheil Feizi, Daniela Massiceti |
| 2023 | Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. Zhang-Wei Hong, Pulkit Agrawal, Remi Tachet des Combes, Romain Laroche |
| 2023 | Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu |
| 2023 | Hebbian Deep Learning Without Feedback. Adrien Journé, Hector Garcia Rodriguez, Qinghai Guo, Timoleon Moraitis |
| 2023 | Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs. Yu Duan, Zhongfan Jia, Qian Li, Yi Zhong, Kaisheng Ma |
| 2023 | Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles. Biswadeep Chakraborty, Saibal Mukhopadhyay |
| 2023 | HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention. Shijie Geng, Jianbo Yuan, Yu Tian, Yuxiao Chen, Yongfeng Zhang |
| 2023 | HiT-MDP: Learning the SMDP option framework on MDPs with Hidden Temporal Embeddings. Chang Li, Dongjin Song, Dacheng Tao |
| 2023 | HiViT: A Simpler and More Efficient Design of Hierarchical Vision Transformer. Xiaosong Zhang, Yunjie Tian, Lingxi Xie, Wei Huang, Qi Dai, Qixiang Ye, Qi Tian |
| 2023 | Hidden Markov Transformer for Simultaneous Machine Translation. Shaolei Zhang, Yang Feng |
| 2023 | Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang |
| 2023 | Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion. Han Wu, Jie Yin, Bala Rajaratnam, Jianyuan Guo |
| 2023 | Hierarchical Sliced Wasserstein Distance. Khai Nguyen, Tongzheng Ren, Huy Nguyen, Litu Rout, Tan Nguyen, Nhat Ho |
| 2023 | Holistic Adversarially Robust Pruning. Qi Zhao, Christian Wressnegger |
| 2023 | HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers. Chen Liang, Haoming Jiang, Zheng Li, Xianfeng Tang, Bing Yin, Tuo Zhao |
| 2023 | HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing. Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew D. Ellington, Alex Dimakis, Adam R. Klivans |
| 2023 | How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu |
| 2023 | How I Learned to Stop Worrying and Love Retraining. Max Zimmer, Christoph Spiegel, Sebastian Pokutta |
| 2023 | How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression? Jetze Schuurmans, Kim Batselier, Julian F. P. Kooij |
| 2023 | How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson |
| 2023 | How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules. Yutong Xie, Ziqiao Xu, Jiaqi Ma, Qiaozhu Mei |
| 2023 | How Sharpness-Aware Minimization Minimizes Sharpness? Kaiyue Wen, Tengyu Ma, Zhiyuan Li |
| 2023 | How gradient estimator variance and bias impact learning in neural networks. Arna Ghosh, Yuhan Helena Liu, Guillaume Lajoie, Konrad P. Körding, Blake Aaron Richards |
| 2023 | How robust is unsupervised representation learning to distribution shift? Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal |
| 2023 | How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? Yifei Ming, Yiyou Sun, Ousmane Dia, Yixuan Li |
| 2023 | How to Train your HIPPO: State Space Models with Generalized Orthogonal Basis Projections. Albert Gu, Isys Johnson, Aman Timalsina, Atri Rudra, Christopher Ré |
| 2023 | How to prepare your task head for finetuning. Yi Ren, Shangmin Guo, Wonho Bae, Danica J. Sutherland |
| 2023 | Human Motion Diffusion Model. Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, Amit Haim Bermano |
| 2023 | Human MotionFormer: Transferring Human Motions with Vision Transformers. Hongyu Liu, Xintong Han, Chenbin Jin, Lihui Qian, Huawei Wei, Zhe Lin, Faqiang Wang, Haoye Dong, Yibing Song, Jia Xu, Qifeng Chen |
| 2023 | Human alignment of neural network representations. Lukas Muttenthaler, Jonas Dippel, Lorenz Linhardt, Robert A. Vandermeulen, Simon Kornblith |
| 2023 | Human-Guided Fair Classification for Natural Language Processing. Florian E. Dorner, Momchil Peychev, Nikola Konstantinov, Naman Goel, Elliott Ash, Martin T. Vechev |
| 2023 | Human-level Atari 200x faster. Steven Kapturowski, Victor Campos, Ray Jiang, Nemanja Rakicevic, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia |
| 2023 | Humanly Certifying Superhuman Classifiers. Qiongkai Xu, Christian Walder, Chenchen Xu |
| 2023 | Hungry Hungry Hippos: Towards Language Modeling with State Space Models. Daniel Y. Fu, Tri Dao, Khaled Kamal Saab, Armin W. Thomas, Atri Rudra, Christopher Ré |
| 2023 | Hybrid RL: Using both offline and online data can make RL efficient. Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, Wen Sun |
| 2023 | HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization. Zefeng Cai, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Xin Alex Lin, Liang He, Daxin Jiang |
| 2023 | Hyper-Decision Transformer for Efficient Online Policy Adaptation. Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, Chuang Gan |
| 2023 | HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork. Jae Yong Lee, Sung Woong Cho, Hyung Ju Hwang |
| 2023 | Hyperbolic Deep Reinforcement Learning. Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt |
| 2023 | Hyperbolic Self-paced Learning for Self-supervised Skeleton-based Action Representations. Luca Franco, Paolo Mandica, Bharti Munjal, Fabio Galasso |
| 2023 | Hyperparameter Optimization through Neural Network Partitioning. Bruno Mlodozeniec, Matthias Reisser, Christos Louizos |
| 2023 | IDEAL: Query-Efficient Data-Free Learning from Black-Box Models. Jie Zhang, Chen Chen, Lingjuan Lyu |
| 2023 | ILA-DA: Improving Transferability of Intermediate Level Attack with Data Augmentation. Chiu Wai Yan, Tsz-Him Cheung, Dit-Yan Yeung |
| 2023 | ISAAC Newton: Input-based Approximate Curvature for Newton's Method. Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen |
| 2023 | ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation. Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, Chi-Wing Fu |
| 2023 | Identifiability Results for Multimodal Contrastive Learning. Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt |
| 2023 | Image as Set of Points. Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun Fu |
| 2023 | Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction. David Klee, Ondrej Biza, Robert Platt, Robin Walters |
| 2023 | ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim |
| 2023 | Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules. Kazuki Irie, Jürgen Schmidhuber |
| 2023 | ImaginaryNet: Learning Object Detectors without Real Images and Annotations. Minheng Ni, Zitong Huang, Kailai Feng, Wangmeng Zuo |
| 2023 | Imbalanced Semi-supervised Learning with Bias Adaptive Classifier. Renzhen Wang, Xixi Jia, Quanziang Wang, Yichen Wu, Deyu Meng |
| 2023 | Imitating Graph-Based Planning with Goal-Conditioned Policies. Junsu Kim, Younggyo Seo, Sungsoo Ahn, Kyunghwan Son, Jinwoo Shin |
| 2023 | Imitating Human Behaviour with Diffusion Models. Tim Pearce, Tabish Rashid, Anssi Kanervisto, David Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin |
| 2023 | Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu |
| 2023 | Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions. Arthur Jacot |
| 2023 | Implicit Regularization for Group Sparsity. Jiangyuan Li, Thanh Van Nguyen, Chinmay Hegde, Raymond K. W. Wong |
| 2023 | Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent. Avrajit Ghosh, He Lyu, Xitong Zhang, Rongrong Wang |
| 2023 | Impossibly Good Experts and How to Follow Them. Aaron Walsman, Muru Zhang, Sanjiban Choudhury, Dieter Fox, Ali Farhadi |
| 2023 | Improved Convergence of Differential Private SGD with Gradient Clipping. Huang Fang, Xiaoyun Li, Chenglin Fan, Ping Li |
| 2023 | Improved Learning-augmented Algorithms for k-means and k-medians Clustering. Thy Dinh Nguyen, Anamay Chaturvedi, Huy L. Nguyen |
| 2023 | Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs. Yuan Cheng, Ruiquan Huang, Yingbin Liang, Jing Yang |
| 2023 | Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography. Akarsh Pokkunuru, Pedram Rooshenas, Thilo Strauss, Anuj Abhishek, Taufiquar Khan |
| 2023 | Improving Deep Policy Gradients with Value Function Search. Enrico Marchesini, Christopher Amato |
| 2023 | Improving Deep Regression with Ordinal Entropy. Shihao Zhang, Linlin Yang, Michael Bi Mi, Xiaoxu Zheng, Angela Yao |
| 2023 | Improving Differentiable Neural Architecture Search by Encouraging Transferability. Parth Sheth, Pengtao Xie |
| 2023 | Improving Object-centric Learning with Query Optimization. Baoxiong Jia, Yu Liu, Siyuan Huang |
| 2023 | Improving Out-of-distribution Generalization with Indirection Representations. Kha Pham, Hung Le, Man Ngo, Truyen Tran |
| 2023 | Improving the imputation of missing data with Markov Blanket discovery. Yang Liu, Anthony C. Constantinou |
| 2023 | In-Situ Text-Only Adaptation of Speech Models with Low-Overhead Speech Imputations. Ashish R. Mittal, Sunita Sarawagi, Preethi Jyothi |
| 2023 | In-context Reinforcement Learning with Algorithm Distillation. Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih |
| 2023 | In-sample Actor Critic for Offline Reinforcement Learning. Hongchang Zhang, Yixiu Mao, Boyuan Wang, Shuncheng He, Yi Xu, Xiangyang Ji |
| 2023 | InCoder: A Generative Model for Code Infilling and Synthesis. Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, Mike Lewis |
| 2023 | InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learning. Zhuoran Yu, Yin Li, Yong Jae Lee |
| 2023 | Incompatibility Clustering as a Defense Against Backdoor Poisoning Attacks. Charles Jin, Melinda Sun, Martin C. Rinard |
| 2023 | Incremental Learning of Structured Memory via Closed-Loop Transcription. Shengbang Tong, Xili Dai, Ziyang Wu, Mingyang Li, Brent Yi, Yi Ma |
| 2023 | Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning. Hao He, Kaiwen Zha, Dina Katabi |
| 2023 | Individual Privacy Accounting with Gaussian Differential Privacy. Antti Koskela, Marlon Tobaben, Antti Honkela |
| 2023 | Inequality phenomenon in l Ranjie Duan, Yuefeng Chen, Yao Zhu, Xiaojun Jia, Rong Zhang, Hui Xue |
| 2023 | Information Plane Analysis for Dropout Neural Networks. Linara Adilova, Bernhard C. Geiger, Asja Fischer |
| 2023 | Information-Theoretic Analysis of Unsupervised Domain Adaptation. Ziqiao Wang, Yongyi Mao |
| 2023 | Information-Theoretic Diffusion. Xianghao Kong, Rob Brekelmans, Greg Ver Steeg |
| 2023 | Instance-wise Batch Label Restoration via Gradients in Federated Learning. Kailang Ma, Yu Sun, Jian Cui, Dawei Li, Zhenyu Guan, Jianwei Liu |
| 2023 | Integrating Symmetry into Differentiable Planning with Steerable Convolutions. Linfeng Zhao, Xupeng Zhu, Lingzhi Kong, Robin Walters, Lawson L. S. Wong |
| 2023 | Interaction-Based Disentanglement of Entities for Object-Centric World Models. Akihiro Nakano, Masahiro Suzuki, Yutaka Matsuo |
| 2023 | Interactive Portrait Harmonization. Jeya Maria Jose Valanarasu, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, Jose Echevarria, Yinglan Ma, Zijun Wei, Kalyan Sunkavalli, Vishal Patel |
| 2023 | Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation. David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii |
| 2023 | Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small. Kevin Ro Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, Jacob Steinhardt |
| 2023 | Interpretability with full complexity by constraining feature information. Kieran A. Murphy, Danielle S. Bassett |
| 2023 | Interpretable Debiasing of Vectorized Language Representations with Iterative Orthogonalization. Prince Osei Aboagye, Yan Zheng, Jack Shunn, Chin-Chia Michael Yeh, Junpeng Wang, Zhongfang Zhuang, Huiyuan Chen, Liang Wang, Wei Zhang, Jeff M. Phillips |
| 2023 | Interpretable Geometric Deep Learning via Learnable Randomness Injection. Siqi Miao, Yunan Luo, Mia Liu, Pan Li |
| 2023 | Interpretations of Domain Adaptations via Layer Variational Analysis. Huan-Hsin Tseng, Hsin-Yi Lin, Kuo-Hsuan Hung, Yu Tsao |
| 2023 | Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare |
| 2023 | Is Adversarial Training Really a Silver Bullet for Mitigating Data Poisoning? Rui Wen, Zhengyu Zhao, Zhuoran Liu, Michael Backes, Tianhao Wang, Yang Zhang |
| 2023 | Is Attention All That NeRF Needs? Mukund Varma T., Peihao Wang, Xuxi Chen, Tianlong Chen, Subhashini Venugopalan, Zhangyang Wang |
| 2023 | Is Conditional Generative Modeling all you need for Decision Making? Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, Pulkit Agrawal |
| 2023 | Is Forgetting Less a Good Inductive Bias for Forward Transfer? Jiefeng Chen, Timothy Nguyen, Dilan Görür, Arslan Chaudhry |
| 2023 | Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function. Ruijie Zheng, Xiyao Wang, Huazhe Xu, Furong Huang |
| 2023 | Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization. Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, Yejin Choi |
| 2023 | Is Synthetic Data from Generative Models Ready for Image Recognition? Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip H. S. Torr, Song Bai, Xiaojuan Qi |
| 2023 | Is a Caption Worth a Thousand Images? A Study on Representation Learning. Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, Tatsunori Hashimoto |
| 2023 | Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama |
| 2023 | Iterative Circuit Repair Against Formal Specifications. Matthias Cosler, Frederik Schmitt, Christopher Hahn, Bernd Finkbeiner |
| 2023 | Iterative Patch Selection for High-Resolution Image Recognition. Benjamin Bergner, Christoph Lippert, Aravindh Mahendran |
| 2023 | Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks. Shuai Zhang, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu, Miao Liu |
| 2023 | Jointly Learning Visual and Auditory Speech Representations from Raw Data. Alexandros Haliassos, Pingchuan Ma, Rodrigo Mira, Stavros Petridis, Maja Pantic |
| 2023 | Kernel Neural Optimal Transport. Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev |
| 2023 | KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP. Yufei Wang, Jiayi Zheng, Can Xu, Xiubo Geng, Tao Shen, Chongyang Tao, Daxin Jiang |
| 2023 | Knowledge Distillation based Degradation Estimation for Blind Super-Resolution. Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, Luc Van Gool |
| 2023 | Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models. Xiaoman Pan, Wenlin Yao, Hongming Zhang, Dian Yu, Dong Yu, Jianshu Chen |
| 2023 | Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts. Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu |
| 2023 | KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals. Sandeep Silwal, Sara Ahmadian, Andrew Nystrom, Andrew McCallum, Deepak Ramachandran, Seyed Mehran Kazemi |
| 2023 | LAVA: Data Valuation without Pre-Specified Learning Algorithms. Hoang Anh Just, Feiyang Kang, Tianhao Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia |
| 2023 | LDMIC: Learning-based Distributed Multi-view Image Coding. Xinjie Zhang, Jiawei Shao, Jun Zhang |
| 2023 | LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence. Zhihao Shi, Xize Liang, Jie Wang |
| 2023 | LMSeg: Language-guided Multi-dataset Segmentation. Qiang Zhou, Yuang Liu, Chaohui Yu, Jingliang Li, Zhibin Wang, Fan Wang |
| 2023 | LPT: Long-tailed Prompt Tuning for Image Classification. Bowen Dong, Pan Zhou, Shuicheng Yan, Wangmeng Zuo |
| 2023 | LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning. Firas Al-Hafez, Davide Tateo, Oleg Arenz, Guoping Zhao, Jan Peters |
| 2023 | Label Propagation with Weak Supervision. Rattana Pukdee, Dylan Sam, Pradeep Kumar Ravikumar, Nina Balcan |
| 2023 | Label-free Concept Bottleneck Models. Tuomas P. Oikarinen, Subhro Das, Lam M. Nguyen, Tsui-Wei Weng |
| 2023 | Language Modelling with Pixels. Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, Desmond Elliott |
| 2023 | Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought. Abulhair Saparov, He He |
| 2023 | Language Models Can Teach Themselves to Program Better. Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai |
| 2023 | Language Models are Realistic Tabular Data Generators. Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, Gjergji Kasneci |
| 2023 | Language models are multilingual chain-of-thought reasoners. Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, Jason Wei |
| 2023 | Large Language Models are Human-Level Prompt Engineers. Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, Jimmy Ba |
| 2023 | Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson |
| 2023 | Latent Bottlenecked Attentive Neural Processes. Leo Feng, Hossein Hajimirsadeghi, Yoshua Bengio, Mohamed Osama Ahmed |
| 2023 | Latent Graph Inference using Product Manifolds. Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Liò |
| 2023 | Latent Neural ODEs with Sparse Bayesian Multiple Shooting. Valerii Iakovlev, Çagatay Yildiz, Markus Heinonen, Harri Lähdesmäki |
| 2023 | Latent State Marginalization as a Low-cost Approach for Improving Exploration. Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen |
| 2023 | Latent Variable Representation for Reinforcement Learning. Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai |
| 2023 | Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations. Ziyu Jiang, Yinpeng Chen, Mengchen Liu, Dongdong Chen, Xiyang Dai, Lu Yuan, Zicheng Liu, Zhangyang Wang |
| 2023 | Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection. Jiajun Fan, Yuzheng Zhuang, Yuecheng Liu, Jianye Hao, Bin Wang, Jiangcheng Zhu, Hao Wang, Shu-Tao Xia |
| 2023 | Learnable Graph Convolutional Attention Networks. Adrián Javaloy, Pablo Sánchez-Martín, Amit Levi, Isabel Valera |
| 2023 | Learnable Topological Features For Phylogenetic Inference via Graph Neural Networks. Cheng Zhang |
| 2023 | Learned Index with Dynamic $\epsilon$. Daoyuan Chen, Wuchao Li, Yaliang Li, Bolin Ding, Kai Zeng, Defu Lian, Jingren Zhou |
| 2023 | Learning About Progress From Experts. Jake Bruce, Ankit Anand, Bogdan Mazoure, Rob Fergus |
| 2023 | Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward. Zihan Zhou, Animesh Garg |
| 2023 | Learning Adversarial Linear Mixture Markov Decision Processes with Bandit Feedback and Unknown Transition. Canzhe Zhao, Ruofeng Yang, Baoxiang Wang, Shuai Li |
| 2023 | Learning Continuous Normalizing Flows For Faster Convergence To Target Distribution via Ascent Regularizations. Shuangshuang Chen, Sihao Ding, Yiannis Karayiannidis, Mårten Björkman |
| 2023 | Learning Controllable Adaptive Simulation for Multi-resolution Physics. Tailin Wu, Takashi Maruyama, Qingqing Zhao, Gordon Wetzstein, Jure Leskovec |
| 2023 | Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model. Zhihai Wang, Xijun Li, Jie Wang, Yufei Kuang, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu |
| 2023 | Learning Diffusion Bridges on Constrained Domains. Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu |
| 2023 | Learning Domain-Agnostic Representation for Disease Diagnosis. Chu-ran Wang, Jing Li, Xinwei Sun, Fandong Zhang, Yizhou Yu, Yizhou Wang |
| 2023 | Learning Fair Graph Representations via Automated Data Augmentations. Hongyi Ling, Zhimeng Jiang, Youzhi Luo, Shuiwang Ji, Na Zou |
| 2023 | Learning Fast and Slow for Online Time Series Forecasting. Quang Pham, Chenghao Liu, Doyen Sahoo, Steven C. H. Hoi |
| 2023 | Learning Group Importance using the Differentiable Hypergeometric Distribution. Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt |
| 2023 | Learning Harmonic Molecular Representations on Riemannian Manifold. Yiqun Wang, Yuning Shen, Shi Chen, Lihao Wang, Fei Ye, Hao Zhou |
| 2023 | Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network. Seungwoong Ha, Hawoong Jeong |
| 2023 | Learning Hierarchical Protein Representations via Complete 3D Graph Networks. Limei Wang, Haoran Liu, Yi Liu, Jerry Kurtin, Shuiwang Ji |
| 2023 | Learning Human-Compatible Representations for Case-Based Decision Support. Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan |
| 2023 | Learning Hyper Label Model for Programmatic Weak Supervision. Renzhi Wu, Shen-En Chen, Jieyu Zhang, Xu Chu |
| 2023 | Learning Input-agnostic Manipulation Directions in StyleGAN with Text Guidance. Yoonjeon Kim, Hyunsu Kim, Junho Kim, Yunjey Choi, Eunho Yang |
| 2023 | Learning Iterative Neural Optimizers for Image Steganography. Xiangyu Chen, Varsha Kishore, Kilian Q. Weinberger |
| 2023 | Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment. Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang |
| 2023 | Learning Label Encodings for Deep Regression. Deval Shah, Tor M. Aamodt |
| 2023 | Learning Language Representations with Logical Inductive Bias. Jianshu Chen |
| 2023 | Learning Locality and Isotropy in Dialogue Modeling. Han Wu, Haochen Tan, Mingjie Zhan, Gangming Zhao, Shaoqing Lu, Ding Liang, Linqi Song |
| 2023 | Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets. Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes, Nadav Cohen, Amir Globerson |
| 2023 | Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency. Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla |
| 2023 | Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions. Ansong Ni, Jeevana Priya Inala, Chenglong Wang, Alex Polozov, Christopher Meek, Dragomir Radev, Jianfeng Gao |
| 2023 | Learning Multimodal Data Augmentation in Feature Space. Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson |
| 2023 | Learning Object-Language Alignments for Open-Vocabulary Object Detection. Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, Jianfei Cai |
| 2023 | Learning Probabilistic Topological Representations Using Discrete Morse Theory. Xiaoling Hu, Dimitris Samaras, Chao Chen |
| 2023 | Learning Proximal Operators to Discover Multiple Optima. Lingxiao Li, Noam Aigerman, Vladimir G. Kim, Jiajin Li, Kristjan H. Greenewald, Mikhail Yurochkin, Justin Solomon |
| 2023 | Learning Rationalizable Equilibria in Multiplayer Games. Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin |
| 2023 | Learning ReLU networks to high uniform accuracy is intractable. Julius Berner, Philipp Grohs, Felix Voigtländer |
| 2023 | Learning Simultaneous Navigation and Construction in Grid Worlds. Wenyu Han, Haoran Wu, Eisuke Hirota, Alexander Gao, Lerrel Pinto, Ludovic Righetti, Chen Feng |
| 2023 | Learning Soft Constraints From Constrained Expert Demonstrations. Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart |
| 2023 | Learning Sparse Group Models Through Boolean Relaxation. Yijie Wang, Yuan Zhou, Xiaoqing Huang, Kun Huang, Jie Zhang, Jianzhu Ma |
| 2023 | Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization. Stamatios Lefkimmiatis, Iaroslav Koshelev |
| 2023 | Learning Structured Representations by Embedding Class Hierarchy. Siqi Zeng, Remi Tachet des Combes, Han Zhao |
| 2023 | Learning Symbolic Models for Graph-structured Physical Mechanism. Hongzhi Shi, Jingtao Ding, Yufan Cao, Quanming Yao, Li Liu, Yong Li |
| 2023 | Learning Uncertainty for Unknown Domains with Zero-Target-Assumption. Yu Yu, Hassan Sajjad, Jia Xu |
| 2023 | Learning Vortex Dynamics for Fluid Inference and Prediction. Yitong Deng, Hong-Xing Yu, Jiajun Wu, Bo Zhu |
| 2023 | Learning What and Where: Disentangling Location and Identity Tracking Without Supervision. Manuel Traub, Sebastian Otte, Tobias Menge, Matthias Karlbauer, Jannik Thümmel, Martin V. Butz |
| 2023 | Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased. Chao Yu, Jiaxuan Gao, Weilin Liu, Botian Xu, Hao Tang, Jiaqi Yang, Yu Wang, Yi Wu |
| 2023 | Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering. Liyao Li, Haobo Wang, Liangyu Zha, Qingyi Huang, Sai Wu, Gang Chen, Junbo Zhao |
| 2023 | Learning differentiable solvers for systems with hard constraints. Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan |
| 2023 | Learning in temporally structured environments. Matt Jones, Tyler R. Scott, Mengye Ren, Gamaleldin Fathy Elsayed, Katherine L. Hermann, David Mayo, Michael Curtis Mozer |
| 2023 | Learning multi-scale local conditional probability models of images. Zahra Kadkhodaie, Florentin Guth, Stéphane Mallat, Eero P. Simoncelli |
| 2023 | Learning on Large-scale Text-attributed Graphs via Variational Inference. Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang |
| 2023 | Learning rigid dynamics with face interaction graph networks. Kelsey R. Allen, Yulia Rubanova, Tatiana Lopez-Guevara, William Whitney, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Tobias Pfaff |
| 2023 | Learning the Positions in CountSketch. Yi Li, Honghao Lin, Simin Liu, Ali Vakilian, David P. Woodruff |
| 2023 | Learning to CROSS exchange to solve min-max vehicle routing problems. Minjun Kim, Junyoung Park, Jinkyoo Park |
| 2023 | Learning to Compose Soft Prompts for Compositional Zero-Shot Learning. Nihal V. Nayak, Peilin Yu, Stephen H. Bach |
| 2023 | Learning to Decompose Visual Features with Latent Textual Prompts. Feng Wang, Manling Li, Xudong Lin, Hairong Lv, Alexander G. Schwing, Heng Ji |
| 2023 | Learning to Estimate Shapley Values with Vision Transformers. Ian Connick Covert, Chanwoo Kim, Su-In Lee |
| 2023 | Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision. Aleksandra Franz, Barbara Solenthaler, Nils Thuerey |
| 2023 | Learning to Extrapolate: A Transductive Approach. Aviv Netanyahu, Abhishek Gupta, Max Simchowitz, Kaiqing Zhang, Pulkit Agrawal |
| 2023 | Learning to Generate Columns with Application to Vertex Coloring. Yuan Sun, Andreas T. Ernst, Xiaodong Li, Jake Weiner |
| 2023 | Learning to Grow Pretrained Models for Efficient Transformer Training. Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogério Feris, David Daniel Cox, Zhangyang Wang, Yoon Kim |
| 2023 | Learning to Induce Causal Structure. Nan Rosemary Ke, Silvia Chiappa, Jane X. Wang, Jörg Bornschein, Anirudh Goyal, Mélanie Rey, Theophane Weber, Matthew M. Botvinick, Michael Curtis Mozer, Danilo Jimenez Rezende |
| 2023 | Learning to Jointly Share and Prune Weights for Grounding Based Vision and Language Models. Shangqian Gao, Burak Uzkent, Yilin Shen, Heng Huang, Hongxia Jin |
| 2023 | Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference. Souvik Kundu, Shunlin Lu, Yuke Zhang, Jacqueline Tiffany Liu, Peter A. Beerel |
| 2023 | Learning to Segment from Noisy Annotations: A Spatial Correction Approach. Jiachen Yao, Yikai Zhang, Songzhu Zheng, Mayank Goswami, Prateek Prasanna, Chao Chen |
| 2023 | Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer. Zhun Yang, Adam Ishay, Joohyung Lee |
| 2023 | Learning to reason over visual objects. Shanka Subhra Mondal, Taylor Whittington Webb, Jonathan Cohen |
| 2023 | Learning topology-preserving data representations. Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii, Nikita Balabin, Evgeny Burnaev, Serguei Barannikov |
| 2023 | Learning where and when to reason in neuro-symbolic inference. Cristina Cornelio, Jan Stuehmer, Shell Xu Hu, Timothy M. Hospedales |
| 2023 | Learning with Auxiliary Activation for Memory-Efficient Training. Sunghyeon Woo, Dongsuk Jeon |
| 2023 | Learning with Logical Constraints but without Shortcut Satisfaction. Zenan Li, Zehua Liu, Yuan Yao, Jingwei Xu, Taolue Chen, Xiaoxing Ma, Jian Lü |
| 2023 | Learning with Stochastic Orders. Carles Domingo-Enrich, Yair Schiff, Youssef Mroueh |
| 2023 | Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting. Myeongho Jeon, Hyoje Lee, Yedarm Seong, Myungjoo Kang |
| 2023 | Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, Ed H. Chi |
| 2023 | Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction. Daehee Park, Hobin Ryu, Yunseo Yang, Jegyeong Cho, Jiwon Kim, Kuk-Jin Yoon |
| 2023 | Leveraging Importance Weights in Subset Selection. Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang |
| 2023 | Leveraging Large Language Models for Multiple Choice Question Answering. Joshua Robinson, David Wingate |
| 2023 | Leveraging Unlabeled Data to Track Memorization. Mahsa Forouzesh, Hanie Sedghi, Patrick Thiran |
| 2023 | LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval. Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang |
| 2023 | LiftedCL: Lifting Contrastive Learning for Human-Centric Perception. Ziwei Chen, Qiang Li, Xiaofeng Wang, Wankou Yang |
| 2023 | Light Sampling Field and BRDF Representation for Physically-based Neural Rendering. Jing Yang, Hanyuan Xiao, Wenbin Teng, Yunxuan Cai, Yajie Zhao |
| 2023 | LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. Xuheng Cai, Chao Huang, Lianghao Xia, Xubin Ren |
| 2023 | LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification. Sharath Girish, Kamal Gupta, Saurabh Singh, Abhinav Shrivastava |
| 2023 | Limitless Stability for Graph Convolutional Networks. Christian Koke |
| 2023 | Linear Connectivity Reveals Generalization Strategies. Jeevesh Juneja, Rachit Bansal, Kyunghyun Cho, João Sedoc, Naomi Saphra |
| 2023 | Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. Rui Yuan, Simon Shaolei Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao |
| 2023 | Linearly Mapping from Image to Text Space. Jack Merullo, Louis Castricato, Carsten Eickhoff, Ellie Pavlick |
| 2023 | Link Prediction with Non-Contrastive Learning. William Shiao, Zhichun Guo, Tong Zhao, Evangelos E. Papalexakis, Yozen Liu, Neil Shah |
| 2023 | LipsFormer: Introducing Lipschitz Continuity to Vision Transformers. Xianbiao Qi, Jianan Wang, Yihao Chen, Yukai Shi, Lei Zhang |
| 2023 | Liquid Structural State-Space Models. Ramin M. Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Daniela Rus |
| 2023 | Localized Randomized Smoothing for Collective Robustness Certification. Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann |
| 2023 | LogicDP: Creating Labels for Graph Data via Inductive Logic Programming. Yuan Yang, Faramarz Fekri, James Clayton Kerce, Ali Payani |
| 2023 | Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning. Chi Han, Qizheng He, Charles Yu, Xinya Du, Hanghang Tong, Heng Ji |
| 2023 | Logical Message Passing Networks with One-hop Inference on Atomic Formulas. Zihao Wang, Yangqiu Song, Ginny Y. Wong, Simon See |
| 2023 | Long Range Language Modeling via Gated State Spaces. Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur |
| 2023 | Long-Tailed Learning Requires Feature Learning. Thomas Laurent, James von Brecht, Xavier Bresson |
| 2023 | Long-Tailed Partial Label Learning via Dynamic Rebalancing. Feng Hong, Jiangchao Yao, Zhihan Zhou, Ya Zhang, Yanfeng Wang |
| 2023 | Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. Ping-Yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, Tom Goldstein |
| 2023 | Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation. Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar |
| 2023 | Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes. Christian Haase, Christoph Hertrich, Georg Loho |
| 2023 | M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation. Junjie Yang, Xuxi Chen, Tianlong Chen, Zhangyang Wang, Yingbin Liang |
| 2023 | MA-BERT: Towards Matrix Arithmetic-only BERT Inference by Eliminating Complex Non-Linear Functions. Neo Wei Ming, Zhehui Wang, Cheng Liu, Rick Siow Mong Goh, Tao Luo |
| 2023 | MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection. Jiaxun Cui, Xiaomeng Yang, Mulong Luo, Geunbae Lee, Peter Stone, Hsien-Hsin S. Lee, Benjamin Lee, G. Edward Suh, Wenjie Xiong, Yuandong Tian |
| 2023 | MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. Mikayel Samvelyan, Akbir Khan, Michael Dennis, Minqi Jiang, Jack Parker-Holder, Jakob Nicolaus Foerster, Roberta Raileanu, Tim Rocktäschel |
| 2023 | MARS: Meta-learning as Score Matching in the Function Space. Krunoslav Lehman Pavasovic, Jonas Rothfuss, Andreas Krause |
| 2023 | MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors. Chen Huang, Hanlin Goh, Jiatao Gu, Joshua M. Susskind |
| 2023 | MCAL: Minimum Cost Human-Machine Active Labeling. Hang Qiu, Krishna Chintalapudi, Ramesh Govindan |
| 2023 | MECTA: Memory-Economic Continual Test-Time Model Adaptation. Junyuan Hong, Lingjuan Lyu, Jiayu Zhou, Michael Spranger |
| 2023 | MEDFAIR: Benchmarking Fairness for Medical Imaging. Yongshuo Zong, Yongxin Yang, Timothy M. Hospedales |
| 2023 | MEDICAL IMAGE UNDERSTANDING WITH PRETRAINED VISION LANGUAGE MODELS: A COMPREHENSIVE STUDY. Ziyuan Qin, Huahui Yi, Qicheng Lao, Kang Li |
| 2023 | MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting. Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, Yifei Xiao |
| 2023 | MIMT: Masked Image Modeling Transformer for Video Compression. Jinxi Xiang, Kuan Tian, Jun Zhang |
| 2023 | MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization. Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, Neil Shah |
| 2023 | MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises. Emanuele Palumbo, Imant Daunhawer, Julia E. Vogt |
| 2023 | MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models. Chenglin Yang, Siyuan Qiao, Qihang Yu, Xiaoding Yuan, Yukun Zhu, Alan L. Yuille, Hartwig Adam, Liang-Chieh Chen |
| 2023 | MPCFORMER: Fast, Performant and Provate Transformer Inference with MPC. Dacheng Li, Hongyi Wang, Rulin Shao, Han Guo, Eric P. Xing, Hao Zhang |
| 2023 | Machine Unlearning of Federated Clusters. Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana, Olgica Milenkovic |
| 2023 | Make-A-Video: Text-to-Video Generation without Text-Video Data. Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, Yaniv Taigman |
| 2023 | Making Better Decision by Directly Planning in Continuous Control. Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou, Tie-Yan Liu, Houqiang Li |
| 2023 | Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples. Qizhang Li, Yiwen Guo, Wangmeng Zuo, Hao Chen |
| 2023 | Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds. Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon |
| 2023 | ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills. Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiang Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, Hao Su |
| 2023 | ManyDG: Many-domain Generalization for Healthcare Applications. Chaoqi Yang, M. Brandon Westover, Jimeng Sun |
| 2023 | MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction. Bencheng Liao, Shaoyu Chen, Xinggang Wang, Tianheng Cheng, Qian Zhang, Wenyu Liu, Chang Huang |
| 2023 | Markup-to-Image Diffusion Models with Scheduled Sampling. Yuntian Deng, Noriyuki Kojima, Alexander M. Rush |
| 2023 | Martingale Posterior Neural Processes. Hyungi Lee, EungGu Yun, Giung Nam, Edwin Fong, Juho Lee |
| 2023 | MaskFusion: Feature Augmentation for Click-Through Rate Prediction via Input-adaptive Mask Fusion. Chao Liao, Jianchao Tan, Jiyuan Jia, Yi Guo, Chengru Song |
| 2023 | MaskViT: Masked Visual Pre-Training for Video Prediction. Agrim Gupta, Stephen Tian, Yunzhi Zhang, Jiajun Wu, Roberto Martín-Martín, Li Fei-Fei |
| 2023 | Masked Distillation with Receptive Tokens. Tao Huang, Yuan Zhang, Shan You, Fei Wang, Chen Qian, Jian Cao, Chang Xu |
| 2023 | Masked Frequency Modeling for Self-Supervised Visual Pre-Training. Jiahao Xie, Wei Li, Xiaohang Zhan, Ziwei Liu, Yew-Soon Ong, Chen Change Loy |
| 2023 | Masked Image Modeling with Denoising Contrast. Kun Yi, Yixiao Ge, Xiaotong Li, Shusheng Yang, Dian Li, Jianping Wu, Ying Shan, Xiaohu Qie |
| 2023 | Masked Unsupervised Self-training for Label-free Image Classification. Junnan Li, Silvio Savarese, Steven C. H. Hoi |
| 2023 | Masked Vision and Language Modeling for Multi-modal Representation Learning. Gukyeong Kwon, Zhaowei Cai, Avinash Ravichandran, Erhan Bas, Rahul Bhotika, Stefano Soatto |
| 2023 | Mass-Editing Memory in a Transformer. Kevin Meng, Arnab Sen Sharma, Alex J. Andonian, Yonatan Belinkov, David Bau |
| 2023 | Massively Scaling Heteroscedastic Classifiers. Mark Collier, Rodolphe Jenatton, Basil Mustafa, Neil Houlsby, Jesse Berent, Effrosyni Kokiopoulou |
| 2023 | Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning. Anton Bakhtin, David J. Wu, Adam Lerer, Jonathan Gray, Athul Paul Jacob, Gabriele Farina, Alexander H. Miller, Noam Brown |
| 2023 | Matching receptor to odorant with protein language and graph neural networks. Matej Hladis, Maxence Lalis, Sébastien Fiorucci, Jérémie Topin |
| 2023 | Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence. Margalit Glasgow, Colin Wei, Mary Wootters, Tengyu Ma |
| 2023 | Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He |
| 2023 | Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition. Junyan Wang, Zhenhong Sun, Yichen Qian, Dong Gong, Xiuyu Sun, Ming Lin, Maurice Pagnucco, Yang Song |
| 2023 | Measure the Predictive Heterogeneity. Jiashuo Liu, Jiayun Wu, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li, Peng Cui |
| 2023 | Measuring Forgetting of Memorized Training Examples. Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang |
| 2023 | Measuring axiomatic soundness of counterfactual image models. Miguel Monteiro, Fabio De Sousa Ribeiro, Nick Pawlowski, Daniel C. Castro, Ben Glocker |
| 2023 | Mega: Moving Average Equipped Gated Attention. Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, Luke Zettlemoyer |
| 2023 | Memorization Capacity of Neural Networks with Conditional Computation. Erdem Koyuncu |
| 2023 | Memorization-Dilation: Modeling Neural Collapse Under Noise. Duc Anh Nguyen, Ron Levie, Julian Lienen, Eyke Hüllermeier, Gitta Kutyniok |
| 2023 | Memory Gym: Partially Observable Challenges to Memory-Based Agents. Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss |
| 2023 | MeshDiffusion: Score-based Generative 3D Mesh Modeling. Zhen Liu, Yao Feng, Michael J. Black, Derek Nowrouzezahrai, Liam Paull, Weiyang Liu |
| 2023 | Meta Knowledge Condensation for Federated Learning. Ping Liu, Xin Yu, Joey Tianyi Zhou |
| 2023 | Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning. Ivona Najdenkoska, Xiantong Zhen, Marcel Worring |
| 2023 | Meta Temporal Point Processes. Wonho Bae, Mohamed Osama Ahmed, Frederick Tung, Gabriel L. Oliveira |
| 2023 | Meta-Learning in Games. Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Steven Wu, Tuomas Sandholm |
| 2023 | Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato |
| 2023 | Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets. Hayeon Lee, Sohyun An, Minseon Kim, Sung Ju Hwang |
| 2023 | MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning. Namyong Park, Ryan A. Rossi, Nesreen K. Ahmed, Christos Faloutsos |
| 2023 | Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics. Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj, David Krueger, Sara Hooker |
| 2023 | Mid-Vision Feedback. Michael Maynord, Eadom Dessalene, Cornelia Fermüller, Yiannis Aloimonos |
| 2023 | Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. Alex Gu, Songtao Lu, Parikshit Ram, Tsui-Wei Weng |
| 2023 | Mind the Gap: Offline Policy Optimization for Imperfect Rewards. Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu, Xianyuan Zhan, Qing-Shan Jia, Ya-Qin Zhang |
| 2023 | Mind the Pool: Convolutional Neural Networks Can Overfit Input Size. Bilal Alsallakh, David Yan, Narine Kokhlikyan, Vivek Miglani, Orion Reblitz-Richardson, Pamela Bhattacharya |
| 2023 | Mind's Eye: Grounded Language Model Reasoning through Simulation. Ruibo Liu, Jason Wei, Shixiang Shane Gu, Te-yen Wu, Soroush Vosoughi, Claire Cui, Denny Zhou, Andrew M. Dai |
| 2023 | Mini-batch k-means terminates within O(d/ϵ) iterations. Gregory Schwartzman |
| 2023 | Minimalistic Unsupervised Representation Learning with the Sparse Manifold Transform. Yubei Chen, Zeyu Yun, Yi Ma, Bruno A. Olshausen, Yann LeCun |
| 2023 | Minimax Optimal Kernel Operator Learning via Multilevel Training. Jikai Jin, Yiping Lu, José H. Blanchet, Lexing Ying |
| 2023 | Minimum Description Length Control. Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matt M. Botvinick |
| 2023 | Minimum Variance Unbiased N: M Sparsity for the Neural Gradients. Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry |
| 2023 | Mitigating Dataset Bias by Using Per-Sample Gradient. Sumyeong Ahn, Seongyoon Kim, Se-Young Yun |
| 2023 | Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach. Heshan Devaka Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Tianyi Chen |
| 2023 | Mitigating Memorization of Noisy Labels via Regularization between Representations. Hao Cheng, Zhaowei Zhu, Xing Sun, Yang Liu |
| 2023 | MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer. Qihao Zhao, Yangyu Huang, Wei Hu, Fan Zhang, Jun Liu |
| 2023 | MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations. Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran |
| 2023 | MocoSFL: enabling cross-client collaborative self-supervised learning. Jingtao Li, Lingjuan Lyu, Daisuke Iso, Chaitali Chakrabarti, Michael Spranger |
| 2023 | Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning. Xiangyu Peng, Chen Xing, Prafulla Kumar Choubey, Chien-Sheng Wu, Caiming Xiong |
| 2023 | Model-based Causal Bayesian Optimization. Scott Sussex, Anastasia Makarova, Andreas Krause |
| 2023 | Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts. Zhitong Gao, Yucong Chen, Chuyu Zhang, Xuming He |
| 2023 | Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval. Shunyu Zhang, Yaobo Liang, Ming Gong, Daxin Jiang, Nan Duan |
| 2023 | Modeling content creator incentives on algorithm-curated platforms. Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, Sarah Dean |
| 2023 | Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. Jivat Neet Kaur, Emre Kiciman, Amit Sharma |
| 2023 | Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN. David M. Knigge, David W. Romero, Albert Gu, Efstratios Gavves, Erik J. Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn, Jan-Jakob Sonke |
| 2023 | Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning. Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, Tongliang Liu |
| 2023 | Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules. Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, Stan Z. Li |
| 2023 | Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching. Shengchao Liu, Hongyu Guo, Jian Tang |
| 2023 | Molecule Generation For Target Protein Binding with Structural Motifs. Zaixi Zhang, Yaosen Min, Shuxin Zheng, Qi Liu |
| 2023 | Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport. Lingkai Kong, Yuqing Wang, Molei Tao |
| 2023 | Monocular Scene Reconstruction with 3D SDF Transformers. Weihao Yuan, Xiaodong Gu, Heng Li, Zilong Dong, Siyu Zhu |
| 2023 | More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization. Jiangxing Wang, Deheng Ye, Zongqing Lu |
| 2023 | More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Tommi Kärkkäinen, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang |
| 2023 | Mosaic Representation Learning for Self-supervised Visual Pre-training. Zhaoqing Wang, Ziyu Chen, Yaqian Li, Yandong Guo, Jun Yu, Mingming Gong, Tongliang Liu |
| 2023 | Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics. Kuo-Hao Zeng, Luca Weihs, Roozbeh Mottaghi, Ali Farhadi |
| 2023 | Multi-Objective Online Learning. Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Lihong Gu, Xiaodong Zeng, Wenwu Zhu |
| 2023 | Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality. Haoye Lu, Daniel Herman, Yaoliang Yu |
| 2023 | Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve. Juhan Bae, Michael R. Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger Baker Grosse |
| 2023 | Multi-domain image generation and translation with identifiability guarantees. Shaoan Xie, Lingjing Kong, Mingming Gong, Kun Zhang |
| 2023 | Multi-level Protein Structure Pre-training via Prompt Learning. Zeyuan Wang, Qiang Zhang, Shuangwei Hu, Haoran Yu, Xurui Jin, Zhichen Gong, Huajun Chen |
| 2023 | Multi-lingual Evaluation of Code Generation Models. Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati |
| 2023 | Multi-objective optimization via equivariant deep hypervolume approximation. Jim Boelrijk, Bernd Ensing, Patrick Forré |
| 2023 | Multi-skill Mobile Manipulation for Object Rearrangement. Jiayuan Gu, Devendra Singh Chaplot, Hao Su, Jitendra Malik |
| 2023 | Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization. Mingxuan Ju, Tong Zhao, Qianlong Wen, Wenhao Yu, Neil Shah, Yanfang Ye, Chuxu Zhang |
| 2023 | MultiViz: Towards Visualizing and Understanding Multimodal Models. Paul Pu Liang, Yiwei Lyu, Gunjan Chhablani, Nihal Jain, Zihao Deng, Xingbo Wang, Louis-Philippe Morency, Ruslan Salakhutdinov |
| 2023 | Multifactor Sequential Disentanglement via Structured Koopman Autoencoders. Nimrod Berman, Ilan Naiman, Omri Azencot |
| 2023 | Multimodal Analogical Reasoning over Knowledge Graphs. Ningyu Zhang, Lei Li, Xiang Chen, Xiaozhuan Liang, Shumin Deng, Huajun Chen |
| 2023 | Multimodal Federated Learning via Contrastive Representation Ensemble. Qiying Yu, Yang Liu, Yimu Wang, Ke Xu, Jingjing Liu |
| 2023 | Multiple sequence alignment as a sequence-to-sequence learning problem. Edo Dotan, Yonatan Belinkov, Oren Avram, Elya Wygoda, Noa Ecker, Michael Alburquerque, Omri Keren, Gil Loewenthal, Tal Pupko |
| 2023 | Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning. Zhen Wang, Rameswar Panda, Leonid Karlinsky, Rogério Feris, Huan Sun, Yoon Kim |
| 2023 | Multivariate Time-series Imputation with Disentangled Temporal Representations. Shuai Liu, Xiucheng Li, Gao Cong, Yile Chen, Yue Jiang |
| 2023 | Mutual Partial Label Learning with Competitive Label Noise. Yan Yan, Yuhong Guo |
| 2023 | NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs. Jinsong Chen, Kaiyuan Gao, Gaichao Li, Kun He |
| 2023 | NANSY++: Unified Voice Synthesis with Neural Analysis and Synthesis. Hyeong-Seok Choi, Jinhyeok Yang, Juheon Lee, Hyeongju Kim |
| 2023 | NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs. Heewon Kim, Kyoung Mu Lee |
| 2023 | NORM: Knowledge Distillation via N-to-One Representation Matching. Xiaolong Liu, Lujun Li, Chao Li, Anbang Yao |
| 2023 | NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning. Ruiqi Ni, Ahmed H. Qureshi |
| 2023 | NTK-SAP: Improving neural network pruning by aligning training dynamics. Yite Wang, Dawei Li, Ruoyu Sun |
| 2023 | NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes. Zhiwen Fan, Peihao Wang, Yifan Jiang, Xinyu Gong, Dejia Xu, Zhangyang Wang |
| 2023 | NeRN: Learning Neural Representations for Neural Networks. Maor Ashkenazi, Zohar Rimon, Ron Vainshtein, Shir Levi, Elad Richardson, Pinchas Mintz, Eran Treister |
| 2023 | Near-Optimal Adversarial Reinforcement Learning with Switching Costs. Ming Shi, Yingbin Liang, Ness B. Shroff |
| 2023 | Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation. Dan Qiao, Yu-Xiang Wang |
| 2023 | Near-optimal Coresets for Robust Clustering. Lingxiao Huang, Shaofeng H.-C. Jiang, Jianing Lou, Xuan Wu |
| 2023 | Near-optimal Policy Identification in Active Reinforcement Learning. Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic |
| 2023 | Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang |
| 2023 | Neural Agents Struggle to Take Turns in Bidirectional Emergent Communication. Valentin Taillandier, Dieuwke Hupkes, Benoît Sagot, Emmanuel Dupoux, Paul Michel |
| 2023 | Neural Architecture Design and Robustness: A Dataset. Steffen Jung, Jovita Lukasik, Margret Keuper |
| 2023 | Neural Bregman Divergences for Distance Learning. Fred Lu, Edward Raff, Francis Ferraro |
| 2023 | Neural Causal Models for Counterfactual Identification and Estimation. Kevin Muyuan Xia, Yushu Pan, Elias Bareinboim |
| 2023 | Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning. Yibo Yang, Haobo Yuan, Xiangtai Li, Zhouchen Lin, Philip H. S. Torr, Dacheng Tao |
| 2023 | Neural Compositional Rule Learning for Knowledge Graph Reasoning. Kewei Cheng, Nesreen K. Ahmed, Yizhou Sun |
| 2023 | Neural DAG Scheduling via One-Shot Priority Sampling. Wonseok Jeon, Mukul Gagrani, Burak Bartan, Weiliang Will Zeng, Harris Teague, Piero Zappi, Christopher Lott |
| 2023 | Neural Design for Genetic Perturbation Experiments. Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis F. Voloch |
| 2023 | Neural Episodic Control with State Abstraction. Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao |
| 2023 | Neural Groundplans: Persistent Neural Scene Representations from a Single Image. Prafull Sharma, Ayush Tewari, Yilun Du, Sergey Zakharov, Rares Andrei Ambrus, Adrien Gaidon, William T. Freeman, Frédo Durand, Joshua B. Tenenbaum, Vincent Sitzmann |
| 2023 | Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling. Youngjoong Kwon, Dahun Kim, Duygu Ceylan, Henry Fuchs |
| 2023 | Neural Implicit Shape Editing using Boundary Sensitivity. Arturs Berzins, Moritz Ibing, Leif Kobbelt |
| 2023 | Neural Lagrangian Schrödinger Bridge: Diffusion Modeling for Population Dynamics. Takeshi Koshizuka, Issei Sato |
| 2023 | Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. Alireza Mousavi Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu |
| 2023 | Neural Networks and the Chomsky Hierarchy. Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Chris Cundy, Marcus Hutter, Shane Legg, Joel Veness, Pedro A. Ortega |
| 2023 | Neural Optimal Transport. Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev |
| 2023 | Neural Radiance Field Codebooks. Matthew Wallingford, Aditya Kusupati, Alex Fang, Vivek Ramanujan, Aniruddha Kembhavi, Roozbeh Mottaghi, Ali Farhadi |
| 2023 | Neural Systematic Binder. Gautam Singh, Yeongbin Kim, Sungjin Ahn |
| 2023 | Neural ePDOs: Spatially Adaptive Equivariant Partial Differential Operator Based Networks. Lingshen He, Yuxuan Chen, Zhengyang Shen, Yibo Yang, Zhouchen Lin |
| 2023 | Neural-based classification rule learning for sequential data. Marine Collery, Philippe Bonnard, François Fages, Remy Kusters |
| 2023 | Neuro-Symbolic Procedural Planning with Commonsense Prompting. Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel P. Eckstein, William Yang Wang |
| 2023 | Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. Félix Chalumeau, Raphaël Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot |
| 2023 | Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams |
| 2023 | New Insights for the Stability-Plasticity Dilemma in Online Continual Learning. Dahuin Jung, Dongjin Lee, Sunwon Hong, Hyemi Jang, Ho Bae, Sungroh Yoon |
| 2023 | No Reason for No Supervision: Improved Generalization in Supervised Models. Mert Bülent Sariyildiz, Yannis Kalantidis, Karteek Alahari, Diane Larlus |
| 2023 | Noise Injection Node Regularization for Robust Learning. Noam Levi, Itay M. Bloch, Marat Freytsis, Tomer Volansky |
| 2023 | Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be. Frederik Kunstner, Jacques Chen, Jonathan Wilder Lavington, Mark Schmidt |
| 2023 | Noise-Robust De-Duplication at Scale. Emily Silcock, Luca D'Amico-Wong, Jinglin Yang, Melissa Dell |
| 2023 | Non-parametric Outlier Synthesis. Leitian Tao, Xuefeng Du, Jerry Zhu, Yixuan Li |
| 2023 | Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities. Samuel Lanthaler, Roberto Molinaro, Patrik Hadorn, Siddhartha Mishra |
| 2023 | Not All Tasks Are Born Equal: Understanding Zero-Shot Generalization. Jing Zhou, Zongyu Lin, Yanan Zheng, Jian Li, Zhilin Yang |
| 2023 | Novel View Synthesis with Diffusion Models. Daniel Watson, William Chan, Ricardo Martin-Brualla, Jonathan Ho, Andrea Tagliasacchi, Mohammad Norouzi |
| 2023 | O(T Yuepeng Yang, Cong Ma |
| 2023 | ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection. Chenyang Zhao, Antoni B. Chan |
| 2023 | OPTQ: Accurate Quantization for Generative Pre-trained Transformers. Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh |
| 2023 | OTOv2: Automatic, Generic, User-Friendly. Tianyi Chen, Luming Liang, Tianyu Ding, Zhihui Zhu, Ilya Zharkov |
| 2023 | Offline Congestion Games: How Feedback Type Affects Data Coverage Requirement. Haozhe Jiang, Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon Shaolei Du |
| 2023 | Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine |
| 2023 | Offline RL for Natural Language Generation with Implicit Language Q Learning. Charlie Snell, Ilya Kostrikov, Yi Su, Sherry Yang, Sergey Levine |
| 2023 | Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization. Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Wai Kin Victor Chan, Xianyuan Zhan |
| 2023 | Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling. Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu |
| 2023 | Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. Ming Yin, Mengdi Wang, Yu-Xiang Wang |
| 2023 | Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework. Corinna Coupette, Sebastian Dalleiger, Bastian Rieck |
| 2023 | Omnigrok: Grokking Beyond Algorithmic Data. Ziming Liu, Eric J. Michaud, Max Tegmark |
| 2023 | On Accelerated Perceptrons and Beyond. Guanghui Wang, Rafael Hanashiro, Etash Kumar Guha, Jacob D. Abernethy |
| 2023 | On Achieving Optimal Adversarial Test Error. Justin D. Li, Matus Telgarsky |
| 2023 | On Compositional Uncertainty Quantification for Seq2seq Graph Parsing. Zi Lin, Du Phan, Panupong Pasupat, Jeremiah Zhe Liu, Jingbo Shang |
| 2023 | On Explaining Neural Network Robustness with Activation Path. Ziping Jiang |
| 2023 | On Pre-training Language Model for Antibody. Danqing Wang, Fei Ye, Hao Zhou |
| 2023 | On Representing Linear Programs by Graph Neural Networks. Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin |
| 2023 | On Representing Mixed-Integer Linear Programs by Graph Neural Networks. Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin |
| 2023 | On The Inadequacy of Optimizing Alignment and Uniformity in Contrastive Learning of Sentence Representations. Zhijie Nie, Richong Zhang, Yongyi Mao |
| 2023 | On The Relative Error of Random Fourier Features for Preserving Kernel Distance. Kuan Cheng, Shaofeng H.-C. Jiang, Luojian Wei, Zhide Wei |
| 2023 | On The Specialization of Neural Modules. Devon Jarvis, Richard Klein, Benjamin Rosman, Andrew M. Saxe |
| 2023 | On amortizing convex conjugates for optimal transport. Brandon Amos |
| 2023 | On the Convergence of AdaGrad(Norm) on ℝ Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy L. Nguyen |
| 2023 | On the Data-Efficiency with Contrastive Image Transformation in Reinforcement Learning. Sicong Liu, Xi Sheryl Zhang, Yushuo Li, Yifan Zhang, Jian Cheng |
| 2023 | On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning. Jianhong Bai, Zuozhu Liu, Hualiang Wang, Jin Hao, Yang Feng, Huanpeng Chu, Haoji Hu |
| 2023 | On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning. Yifan Xu, Nicklas Hansen, Zirui Wang, Yung-Chieh Chan, Hao Su, Zhuowen Tu |
| 2023 | On the Importance and Applicability of Pre-Training for Federated Learning. Hong-You Chen, Cheng-Hao Tu, Ziwei Li, Han-Wei Shen, Wei-Lun Chao |
| 2023 | On the Performance of Temporal Difference Learning With Neural Networks. Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky |
| 2023 | On the Perils of Cascading Robust Classifiers. Ravi Mangal, Zifan Wang, Chi Zhang, Klas Leino, Corina S. Pasareanu, Matt Fredrikson |
| 2023 | On the Robustness of Safe Reinforcement Learning under Observational Perturbations. Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li, Ding Zhao |
| 2023 | On the Saturation Effect of Kernel Ridge Regression. Yicheng Li, Haobo Zhang, Qian Lin |
| 2023 | On the Sensitivity of Reward Inference to Misspecified Human Models. Joey Hong, Kush Bhatia, Anca D. Dragan |
| 2023 | On the Soft-Subnetwork for Few-Shot Class Incremental Learning. Haeyong Kang, Jaehong Yoon, Sultan Rizky Hikmawan Madjid, Sung Ju Hwang, Chang D. Yoo |
| 2023 | On the Trade-Off between Actionable Explanations and the Right to be Forgotten. Martin Pawelczyk, Tobias Leemann, Asia Biega, Gjergji Kasneci |
| 2023 | On the Usefulness of Embeddings, Clusters and Strings for Text Generation Evaluation. Tiago Pimentel, Clara Meister, Ryan Cotterell |
| 2023 | On the Word Boundaries of Emergent Languages Based on Harris's Articulation Scheme. Ryo Ueda, Taiga Ishii, Yusuke Miyao |
| 2023 | On the complexity of nonsmooth automatic differentiation. Jérôme Bolte, Ryan Boustany, Edouard Pauwels, Béatrice Pesquet-Popescu |
| 2023 | On the duality between contrastive and non-contrastive self-supervised learning. Quentin Garrido, Yubei Chen, Adrien Bardes, Laurent Najman, Yann LeCun |
| 2023 | One Transformer Can Understand Both 2D & 3D Molecular Data. Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He |
| 2023 | One-Pixel Shortcut: On the Learning Preference of Deep Neural Networks. Shutong Wu, Sizhe Chen, Cihang Xie, Xiaolin Huang |
| 2023 | Online Bias Correction for Task-Free Continual Learning. Aristotelis Chrysakis, Marie-Francine Moens |
| 2023 | Online Boundary-Free Continual Learning by Scheduled Data Prior. Hyunseo Koh, Minhyuk Seo, Jihwan Bang, Hwanjun Song, Deokki Hong, Seulki Park, Jung-Woo Ha, Jonghyun Choi |
| 2023 | Online Low Rank Matrix Completion. Soumyabrata Pal, Prateek Jain |
| 2023 | Open-Vocabulary Object Detection upon Frozen Vision and Language Models. Weicheng Kuo, Yin Cui, Xiuye Gu, A. J. Piergiovanni, Anelia Angelova |
| 2023 | Optimal Activation Functions for the Random Features Regression Model. Jianxin Wang, José Bento |
| 2023 | Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian. Paria Rashidinejad, Hanlin Zhu, Kunhe Yang, Stuart Russell, Jiantao Jiao |
| 2023 | Optimal Transport for Offline Imitation Learning. Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth |
| 2023 | Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics. Sirui Zheng, Lingxiao Wang, Shuang Qiu, Zuyue Fu, Zhuoran Yang, Csaba Szepesvári, Zhaoran Wang |
| 2023 | Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning. Sheng Zhang, Hao Cheng, Jianfeng Gao, Hoifung Poon |
| 2023 | Optimizing Spca-based Continual Learning: A Theoretical Approach. Chunchun Yang, Malik Tiomoko, Zengfu Wang |
| 2023 | Order Matters: Agent-by-agent Policy Optimization. Xihuai Wang, Zheng Tian, Ziyu Wan, Ying Wen, Jun Wang, Weinan Zhang |
| 2023 | Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing. Yunchong Song, Chenghu Zhou, Xinbing Wang, Zhouhan Lin |
| 2023 | Out-of-Distribution Detection and Selective Generation for Conditional Language Models. Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu |
| 2023 | Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy. Jinsong Zhang, Qiang Fu, Xu Chen, Lun Du, Zelin Li, Gang Wang, Xiaoguang Liu, Shi Han, Dongmei Zhang |
| 2023 | Out-of-distribution Detection with Implicit Outlier Transformation. Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han |
| 2023 | Out-of-distribution Representation Learning for Time Series Classification. Wang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, Xing Xie |
| 2023 | Outcome-directed Reinforcement Learning by Uncertainty \& Temporal Distance-Aware Curriculum Goal Generation. Daesol Cho, Seungjae Lee, H. Jin Kim |
| 2023 | Over-Training with Mixup May Hurt Generalization. Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao |
| 2023 | Over-parameterized Model Optimization with Polyak-Łojasiewicz Condition. Yixuan Chen, Yubin Shi, Mingzhi Dong, Xiaochen Yang, Dongsheng Li, Yujiang Wang, Robert P. Dick, Qin Lv, Yingying Zhao, Fan Yang, Ning Gu, Li Shang |
| 2023 | PAC Reinforcement Learning for Predictive State Representations. Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee |
| 2023 | PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification. Xuan Li, Yi-Ling Qiao, Peter Yichen Chen, Krishna Murthy Jatavallabhula, Ming Lin, Chenfanfu Jiang, Chuang Gan |
| 2023 | PASHA: Efficient HPO and NAS with Progressive Resource Allocation. Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella |
| 2023 | PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm. Toygun Basaklar, Suat Gumussoy, Ümit Y. Ogras |
| 2023 | PEER: A Collaborative Language Model. Timo Schick, Jane A. Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, Sebastian Riedel |
| 2023 | PGrad: Learning Principal Gradients For Domain Generalization. Zhe Wang, Jake Grigsby, Yanjun Qi |
| 2023 | PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales. Peifeng Wang, Aaron Chan, Filip Ilievski, Muhao Chen, Xiang Ren |
| 2023 | PLOT: Prompt Learning with Optimal Transport for Vision-Language Models. Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang |
| 2023 | POPGym: Benchmarking Partially Observable Reinforcement Learning. Steven D. Morad, Ryan Kortvelesy, Matteo Bettini, Stephan Liwicki, Amanda Prorok |
| 2023 | PV3D: A 3D Generative Model for Portrait Video Generation. Eric Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew, Wenqing Zhang, Song Bai, Jiashi Feng, Mike Zheng Shou |
| 2023 | PaLI: A Jointly-Scaled Multilingual Language-Image Model. Xi Chen, Xiao Wang, Soravit Changpinyo, A. J. Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari, Gaurav Mishra, Linting Xue, Ashish V. Thapliyal, James Bradbury, Weicheng Kuo |
| 2023 | Packed Ensembles for efficient uncertainty estimation. Olivier Laurent, Adrien Lafage, Enzo Tartaglione, Geoffrey Daniel, Jean-Marc Martinez, Andrei Bursuc, Gianni Franchi |
| 2023 | PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs. James Oldfield, Christos Tzelepis, Yannis Panagakis, Mihalis Nicolaou, Ioannis Patras |
| 2023 | Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. Hong-Min Chu, Jonas Geiping, Liam H. Fowl, Micah Goldblum, Tom Goldstein |
| 2023 | Parallel Deep Neural Networks Have Zero Duality Gap. Yifei Wang, Tolga Ergen, Mert Pilanci |
| 2023 | Parameter-Efficient Fine-Tuning Design Spaces. Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, Diyi Yang |
| 2023 | Parametrizing Product Shape Manifolds by Composite Networks. Josua Sassen, Klaus Hildebrandt, Martin Rumpf, Benedikt Wirth |
| 2023 | Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng |
| 2023 | Part-Based Models Improve Adversarial Robustness. Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen, Nicholas Carlini, David A. Wagner |
| 2023 | Partial Label Unsupervised Domain Adaptation with Class-Prototype Alignment. Yan Yan, Yuhong Guo |
| 2023 | Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. Fan Chen, Yu Bai, Song Mei |
| 2023 | Particle-based Variational Inference with Preconditioned Functional Gradient Flow. Hanze Dong, Xi Wang, Yong Lin, Tong Zhang |
| 2023 | Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning. Shaofeng Zhang, Feng Zhu, Rui Zhao, Junchi Yan |
| 2023 | PatchDCT: Patch Refinement for High Quality Instance Segmentation. Qinrou Wen, Jirui Yang, Xue Yang, Kewei Liang |
| 2023 | PerFedMask: Personalized Federated Learning with Optimized Masking Vectors. Mehdi Setayesh, Xiaoxiao Li, Vincent W. S. Wong |
| 2023 | Perfectly Secure Steganography Using Minimum Entropy Coupling. Christian Schröder de Witt, Samuel Sokota, J. Zico Kolter, Jakob Nicolaus Foerster, Martin Strohmeier |
| 2023 | Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. Haotian Fu, Jiayu Yao, Omer Gottesman, Finale Doshi-Velez, George Konidaris |
| 2023 | Personalized Federated Learning with Feature Alignment and Classifier Collaboration. Jian Xu, Xinyi Tong, Shao-Lun Huang |
| 2023 | Personalized Reward Learning with Interaction-Grounded Learning (IGL). Jessica Maghakian, Paul Mineiro, Kishan Panaganti, Mark Rucker, Akanksha Saran, Cheng Tan |
| 2023 | Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes. Miao Lu, Yifei Min, Zhaoran Wang, Zhuoran Yang |
| 2023 | Phase transition for detecting a small community in a large network. Jiashun Jin, Zheng Tracy Ke, Paxton Turner, Anru Zhang |
| 2023 | Phase2vec: dynamical systems embedding with a physics-informed convolutional network. Matthew Ricci, Noa Moriel, Zoe Piran, Mor Nitzan |
| 2023 | Phenaki: Variable Length Video Generation from Open Domain Textual Descriptions. Ruben Villegas, Mohammad Babaeizadeh, Pieter-Jan Kindermans, Hernan Moraldo, Han Zhang, Mohammad Taghi Saffar, Santiago Castro, Julius Kunze, Dumitru Erhan |
| 2023 | PiFold: Toward effective and efficient protein inverse folding. Zhangyang Gao, Cheng Tan, Stan Z. Li |
| 2023 | Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning. Onno Eberhard, Jakob J. Hollenstein, Cristina Pinneri, Georg Martius |
| 2023 | Pitfalls of Gaussians as a noise distribution in NCE. Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski |
| 2023 | Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training. Simone Zini, Alex Gomez-Villa, Marco Buzzelli, Bartlomiej Twardowski, Andrew D. Bagdanov, Joost van de Weijer |
| 2023 | Planning Goals for Exploration. Edward S. Hu, Richard Chang, Oleh Rybkin, Dinesh Jayaraman |
| 2023 | Planning with Large Language Models for Code Generation. Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B. Tenenbaum, Chuang Gan |
| 2023 | Planning with Sequence Models through Iterative Energy Minimization. Hongyi Chen, Yilun Du, Yiye Chen, Joshua B. Tenenbaum, Patricio A. Vela |
| 2023 | Plateau in Monotonic Linear Interpolation - A "Biased" View of Loss Landscape for Deep Networks. Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge |
| 2023 | Policy Expansion for Bridging Offline-to-Online Reinforcement Learning. Haichao Zhang, Wei Xu, Haonan Yu |
| 2023 | Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling. Penghao Wu, Li Chen, Hongyang Li, Xiaosong Jia, Junchi Yan, Yu Qiao |
| 2023 | Policy-Based Self-Competition for Planning Problems. Jonathan Pirnay, Quirin Göttl, Jakob Burger, Dominik Gerhard Grimm |
| 2023 | Population-size-Aware Policy Optimization for Mean-Field Games. Pengdeng Li, Xinrun Wang, Shuxin Li, Hau Chan, Bo An |
| 2023 | Post-hoc Concept Bottleneck Models. Mert Yüksekgönül, Maggie Wang, James Zou |
| 2023 | Powderworld: A Platform for Understanding Generalization via Rich Task Distributions. Kevin Frans, Phillip Isola |
| 2023 | PowerQuant: Automorphism Search for Non-Uniform Quantization. Edouard Yvinec, Arnaud Dapogny, Matthieu Cord, Kevin Bailly |
| 2023 | Pre-training via Denoising for Molecular Property Prediction. Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin |
| 2023 | Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information. Yulun Wu, Robert A. Barton, Zichen Wang, Vassilis N. Ioannidis, Carlo De Donno, Layne C. Price, Luis F. Voloch, George Karypis |
| 2023 | Predictive Inference with Feature Conformal Prediction. Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan |
| 2023 | Predictor-corrector algorithms for stochastic optimization under gradual distribution shift. Subha Maity, Debarghya Mukherjee, Moulinath Banerjee, Yuekai Sun |
| 2023 | Preference Transformer: Modeling Human Preferences using Transformers for RL. Changyeon Kim, Jongjin Park, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee |
| 2023 | Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models. Guande He, Jianfei Chen, Jun Zhu |
| 2023 | Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning. Sasha Salter, Kristian Hartikainen, Walter Goodwin, Ingmar Posner |
| 2023 | Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. Andrew Lowy, Meisam Razaviyayn |
| 2023 | Proactive Multi-Camera Collaboration for 3D Human Pose Estimation. Hai Ci, Mickel Liu, Xuehai Pan, Fangwei Zhong, Yizhou Wang |
| 2023 | Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse. Martin Pawelczyk, Teresa Datta, Johannes van den Heuvel, Gjergji Kasneci, Himabindu Lakkaraju |
| 2023 | Programmatically Grounded, Compositionally Generalizable Robotic Manipulation. Renhao Wang, Jiayuan Mao, Joy Hsu, Hang Zhao, Jiajun Wu, Yang Gao |
| 2023 | Progress measures for grokking via mechanistic interpretability. Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, Jacob Steinhardt |
| 2023 | Progressive Mix-Up for Few-Shot Supervised Multi-Source Domain Transfer. Ronghang Zhu, Xiang Yu, Sheng Li |
| 2023 | Progressive Prompts: Continual Learning for Language Models. Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, Amjad Almahairi |
| 2023 | Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning. Chunwei Ma, Zhanghexuan Ji, Ziyun Huang, Yan Shen, Mingchen Gao, Jinhui Xu |
| 2023 | Progressively Compressed Auto-Encoder for Self-supervised Representation Learning. Jin Li, Yaoming Wang, Xiaopeng Zhang, Yabo Chen, Dongsheng Jiang, Wenrui Dai, Chenglin Li, Hongkai Xiong, Qi Tian |
| 2023 | Projective Proximal Gradient Descent for Nonconvex Nonsmooth Optimization: Fast Convergence Without Kurdyka-Lojasiewicz (KL) Property. Yingzhen Yang, Ping Li |
| 2023 | Prompt-to-Prompt Image Editing with Cross-Attention Control. Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, Daniel Cohen-Or |
| 2023 | Promptagator: Few-shot Dense Retrieval From 8 Examples. Zhuyun Dai, Vincent Y. Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, Ming-Wei Chang |
| 2023 | Prompting GPT-3 To Be Reliable. Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan L. Boyd-Graber, Lijuan Wang |
| 2023 | Proposal-Contrastive Pretraining for Object Detection from Fewer Data. Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard |
| 2023 | Protein Representation Learning by Geometric Structure Pretraining. Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang |
| 2023 | Protein Representation Learning via Knowledge Enhanced Primary Structure Reasoning. Hong-Yu Zhou, Yunxiang Fu, Zhicheng Zhang, Cheng Bian, Yizhou Yu |
| 2023 | Protein Sequence and Structure Co-Design with Equivariant Translation. Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, Jian Tang |
| 2023 | Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare |
| 2023 | Prototypical Calibration for Few-shot Learning of Language Models. Zhixiong Han, Yaru Hao, Li Dong, Yutao Sun, Furu Wei |
| 2023 | Provable Defense Against Geometric Transformations. Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh |
| 2023 | Provable Memorization Capacity of Transformers. Junghwan Kim, Michelle Kim, Barzan Mozafari |
| 2023 | Provable Robustness against Wasserstein Distribution Shifts via Input Randomization. Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi |
| 2023 | Provable Sim-to-real Transfer in Continuous Domain with Partial Observations. Jiachen Hu, Han Zhong, Chi Jin, Liwei Wang |
| 2023 | Provably Auditing Ordinary Least Squares in Low Dimensions. Ankur Moitra, Dhruv Rohatgi |
| 2023 | Provably Efficient Lifelong Reinforcement Learning with Linear Representation. Sanae Amani, Lin Yang, Ching-An Cheng |
| 2023 | Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path. Yihan Du, Siwei Wang, Longbo Huang |
| 2023 | Pruning Deep Neural Networks from a Sparsity Perspective. Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh |
| 2023 | Pseudo-label Training and Model Inertia in Neural Machine Translation. Benjamin Hsu, Anna Currey, Xing Niu, Maria Nadejde, Georgiana Dinu |
| 2023 | Pseudoinverse-Guided Diffusion Models for Inverse Problems. Jiaming Song, Arash Vahdat, Morteza Mardani, Jan Kautz |
| 2023 | Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play. Jeremiah Zhe Liu, Krishnamurthy (Dj) Dvijotham, Jihyeon Lee, Quan Yuan, Balaji Lakshminarayanan, Deepak Ramachandran |
| 2023 | Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore. Guoyang Xie, Jinbao Wang, Jiaqi Liu, Yaochu Jin, Feng Zheng |
| 2023 | Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots. Wei Hung, Bo-Kai Huang, Ping-Chun Hsieh, Xi Liu |
| 2023 | QAID: Question Answering Inspired Few-shot Intent Detection. Asaf Yehudai, Matan Vetzler, Yosi Mass, Koren Lazar, Doron Cohen, Boaz Carmeli |
| 2023 | QuAnt: Quantum Annealing with Learnt Couplings. Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk, Zorah Lähner, Michael Moeller, Vladislav Golyanik |
| 2023 | Quality-Similar Diversity via Population Based Reinforcement Learning. Shuang Wu, Jian Yao, Haobo Fu, Ye Tian, Chao Qian, Yaodong Yang, Qiang Fu, Wei Yang |
| 2023 | Quantifying Memorization Across Neural Language Models. Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang |
| 2023 | Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics. Julius Adebayo, Melissa Hall, Bowen Yu, Bobbie Chern |
| 2023 | Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. Jake Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel |
| 2023 | Quantized Compressed Sensing with Score-Based Generative Models. Xiangming Meng, Yoshiyuki Kabashima |
| 2023 | Quasi-optimal Reinforcement Learning with Continuous Actions. Yuhan Li, Wenzhuo Zhou, Ruoqing Zhu |
| 2023 | REPAIR: REnormalizing Permuted Activations for Interpolation Repair. Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur |
| 2023 | RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection. Shancong Mou, Xiaoyi Gu, Meng Cao, Haoping Bai, Ping Huang, Jiulong Shan, Jianjun Shi |
| 2023 | RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch. Yiqin Tan, Pihe Hu, Ling Pan, Jiatai Huang, Longbo Huang |
| 2023 | ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs. Han Lu, Zenan Li, Runzhong Wang, Qibing Ren, Xijun Li, Mingxuan Yuan, Jia Zeng, Xiaokang Yang, Junchi Yan |
| 2023 | ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning. Olga Golovneva, Moya Chen, Spencer Poff, Martin Corredor, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz |
| 2023 | RPM: Generalizable Multi-Agent Policies for Multi-Agent Reinforcement Learning. Wei Qiu, Xiao Ma, Bo An, Svetlana Obraztsova, Shuicheng Yan, Zhongwen Xu |
| 2023 | RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates. Laurent Condat, Peter Richtárik |
| 2023 | Random Laplacian Features for Learning with Hyperbolic Space. Tao Yu, Christopher De Sa |
| 2023 | Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images. Jiyeon Han, Hwanil Choi, Yunjey Choi, Junho Kim, Jung-Woo Ha, Jaesik Choi |
| 2023 | Re-Imagen: Retrieval-Augmented Text-to-Image Generator. Wenhu Chen, Hexiang Hu, Chitwan Saharia, William W. Cohen |
| 2023 | Re-calibrating Feature Attributions for Model Interpretation. Peiyu Yang, Naveed Akhtar, Zeyi Wen, Mubarak Shah, Ajmal Saeed Mian |
| 2023 | Re-parameterizing Your Optimizers rather than Architectures. Xiaohan Ding, Honghao Chen, Xiangyu Zhang, Kaiqi Huang, Jungong Han, Guiguang Ding |
| 2023 | Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization. Sangwon Jung, Taeeon Park, Sanghyuk Chun, Taesup Moon |
| 2023 | ReAct: Synergizing Reasoning and Acting in Language Models. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, Yuan Cao |
| 2023 | Real-Time Image Demoiréing on Mobile Devices. Yuxin Zhang, Mingbao Lin, Xunchao Li, Han Liu, Guozhi Wang, Fei Chao, Shuai Ren, Yafei Wen, Xiaoxin Chen, Rongrong Ji |
| 2023 | Real-time variational method for learning neural trajectory and its dynamics. Matthew Dowling, Yuan Zhao, Il Memming Park |
| 2023 | Recitation-Augmented Language Models. Zhiqing Sun, Xuezhi Wang, Yi Tay, Yiming Yang, Denny Zhou |
| 2023 | Recon: Reducing Conflicting Gradients From the Root For Multi-Task Learning. Guangyuan Shi, Qimai Li, Wenlong Zhang, Jiaxin Chen, Xiao-Ming Wu |
| 2023 | Recursive Time Series Data Augmentation. Amine Mohamed Aboussalah, Min-Jae Kwon, Raj G. Patel, Cheng Chi, Chi-Guhn Lee |
| 2023 | Red PANDA: Disambiguating Image Anomaly Detection by Removing Nuisance Factors. Niv Cohen, Jonathan Kahana, Yedid Hoshen |
| 2023 | Regression with Label Differential Privacy. Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang |
| 2023 | Relational Attention: Generalizing Transformers for Graph-Structured Tasks. Cameron Diao, Ricky Loynd |
| 2023 | Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences. Lin Guan, Karthik Valmeekam, Subbarao Kambhampati |
| 2023 | Relative representations enable zero-shot latent space communication. Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodolà |
| 2023 | Reliability of CKA as a Similarity Measure in Deep Learning. MohammadReza Davari, Stefan Horoi, Amine Natik, Guillaume Lajoie, Guy Wolf, Eugene Belilovsky |
| 2023 | Reparameterization through Spatial Gradient Scaling. Alexander Detkov, Mohammad Salameh, Muhammad Fetrat Qharabagh, Jialin Zhang, Robin Luwei, Shangling Jui, Di Niu |
| 2023 | Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay. Hongming Zhang, Chenjun Xiao, Han Wang, Jun Jin, Bo Xu, Martin Müller |
| 2023 | Replicable Bandits. Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas |
| 2023 | Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency. Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang |
| 2023 | Representation Learning for Low-rank General-sum Markov Games. Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang |
| 2023 | Representational Dissimilarity Metric Spaces for Stochastic Neural Networks. Lyndon R. Duong, Jingyang Zhou, Josue Nassar, Jules Berman, Jeroen Olieslagers, Alex H. Williams |
| 2023 | ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor. Wanqi Xue, Qingpeng Cai, Ruohan Zhan, Dong Zheng, Peng Jiang, Kun Gai, Bo An |
| 2023 | Restricted Strong Convexity of Deep Learning Models with Smooth Activations. Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin |
| 2023 | Rethinking Graph Lottery Tickets: Graph Sparsity Matters. Bo Hui, Da Yan, Xiaolong Ma, Wei-Shinn Ku |
| 2023 | Rethinking Self-Supervised Visual Representation Learning in Pre-training for 3D Human Pose and Shape Estimation. Hongsuk Choi, Hyeongjin Nam, Taeryung Lee, Gyeongsik Moon, Kyoung Mu Lee |
| 2023 | Rethinking Symbolic Regression: Morphology and Adaptability in the Context of Evolutionary Algorithms. Kei Sen Fong, Shelvia Wongso, Mehul Motani |
| 2023 | Rethinking skip connection model as a learnable Markov chain. Dengsheng Chen, Jie Hu, Wenwen Qiang, Xiaoming Wei, Enhua Wu |
| 2023 | Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning. Rundong Luo, Yifei Wang, Yisen Wang |
| 2023 | Rethinking the Expressive Power of GNNs via Graph Biconnectivity. Bohang Zhang, Shengjie Luo, Liwei Wang, Di He |
| 2023 | Retrieval-based Controllable Molecule Generation. Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard G. Baraniuk, Anima Anandkumar |
| 2023 | Reversible Column Networks. Yuxuan Cai, Yizhuang Zhou, Qi Han, Jianjian Sun, Xiangwen Kong, Jun Li, Xiangyu Zhang |
| 2023 | Revisit Finetuning strategy for Few-Shot Learning to Transfer the Emdeddings. Heng Wang, Tan Yue, Xiang Ye, Zihang He, Bohan Li, Yong Li |
| 2023 | Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective. Kuan Li, Yang Liu, Xiang Ao, Qing He |
| 2023 | Revisiting Intrinsic Reward for Exploration in Procedurally Generated Environments. Kaixin Wang, Kuangqi Zhou, Bingyi Kang, Jiashi Feng, Shuicheng Yan |
| 2023 | Revisiting Populations in multi-agent Communication. Paul Michel, Mathieu Rita, Kory Wallace Mathewson, Olivier Tieleman, Angeliki Lazaridou |
| 2023 | Revisiting Pruning at Initialization Through the Lens of Ramanujan Graph. Duc N. M. Hoang, Shiwei Liu, Radu Marculescu, Zhangyang Wang |
| 2023 | Revisiting Robustness in Graph Machine Learning. Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann |
| 2023 | Revisiting adapters with adversarial training. Sylvestre-Alvise Rebuffi, Francesco Croce, Sven Gowal |
| 2023 | Revisiting the Assumption of Latent Separability for Backdoor Defenses. Xiangyu Qi, Tinghao Xie, Yiming Li, Saeed Mahloujifar, Prateek Mittal |
| 2023 | Revisiting the Entropy Semiring for Neural Speech Recognition. Oscar Chang, Dongseong Hwang, Olivier Siohan |
| 2023 | Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. Chang Liu, Zetian Jiang, Runzhong Wang, Lingxiao Huang, Pinyan Lu, Junchi Yan |
| 2023 | Reward Design with Language Models. Minae Kwon, Sang Michael Xie, Kalesha Bullard, Dorsa Sadigh |
| 2023 | Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski |
| 2023 | Riemannian Metric Learning via Optimal Transport. Christopher Scarvelis, Justin Solomon |
| 2023 | Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation. Thanh Lam, Arun Verma, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2023 | RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data. Sangwoo Mo, Jong-Chyi Su, Chih-Yao Ma, Mido Assran, Ishan Misra, Licheng Yu, Sean Bell |
| 2023 | Robust Active Distillation. Cenk Baykal, Khoa Trinh, Fotis Iliopoulos, Gaurav Menghani, Erik Vee |
| 2023 | Robust Algorithms on Adaptive Inputs from Bounded Adversaries. Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Fred Zhang, Qiuyi Zhang, Samson Zhou |
| 2023 | Robust Explanation Constraints for Neural Networks. Matthew Wicker, Juyeon Heo, Luca Costabello, Adrian Weller |
| 2023 | Robust Fair Clustering: A Novel Fairness Attack and Defense Framework. Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, Hongfu Liu |
| 2023 | Robust Graph Dictionary Learning. Weijie Liu, Jiahao Xie, Chao Zhang, Makoto Yamada, Nenggan Zheng, Hui Qian |
| 2023 | Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Luke Huan |
| 2023 | Robust Scheduling with GFlowNets. David W. Zhang, Corrado Rainone, Markus Peschl, Roberto Bondesan |
| 2023 | Robust and Controllable Object-Centric Learning through Energy-based Models. Ruixiang Zhang, Tong Che, Boris Ivanovic, Renhao Wang, Marco Pavone, Yoshua Bengio, Liam Paull |
| 2023 | Robustness to corruption in pre-trained Bayesian neural networks. Xi Wang, Laurence Aitchison |
| 2023 | Rotamer Density Estimator is an Unsupervised Learner of the Effect of Mutations on Protein-Protein Interaction. Shitong Luo, Yufeng Su, Zuofan Wu, Chenpeng Su, Jian Peng, Jianzhu Ma |
| 2023 | S-NeRF: Neural Radiance Fields for Street Views. Ziyang Xie, Junge Zhang, Wenye Li, Feihu Zhang, Li Zhang |
| 2023 | SAM as an Optimal Relaxation of Bayes. Thomas Möllenhoff, Mohammad Emtiyaz Khan |
| 2023 | SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency. Junfeng Guo, Yiming Li, Xun Chen, Hanqing Guo, Lichao Sun, Cong Liu |
| 2023 | SCoMoE: Efficient Mixtures of Experts with Structured Communication. Zhiyuan Zeng, Deyi Xiong |
| 2023 | SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space. Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis |
| 2023 | SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization. Hanseul Cho, Chulhee Yun |
| 2023 | SIMPLE: A Gradient Estimator for k-Subset Sampling. Kareem Ahmed, Zhe Zeng, Mathias Niepert, Guy Van den Broeck |
| 2023 | SIMPLE: Specialized Model-Sample Matching for Domain Generalization. Ziyue Li, Kan Ren, Xinyang Jiang, Yifei Shen, Haipeng Zhang, Dongsheng Li |
| 2023 | SLTUNET: A Simple Unified Model for Sign Language Translation. Biao Zhang, Mathias Müller, Rico Sennrich |
| 2023 | SMART: Self-supervised Multi-task pretrAining with contRol Transformers. Yanchao Sun, Shuang Ma, Ratnesh Madaan, Rogerio Bonatti, Furong Huang, Ashish Kapoor |
| 2023 | SMART: Sentences as Basic Units for Text Evaluation. Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan |
| 2023 | SP2 : A Second Order Stochastic Polyak Method. Shuang Li, William J. Swartworth, Martin Takác, Deanna Needell, Robert M. Gower |
| 2023 | SQA3D: Situated Question Answering in 3D Scenes. Xiaojian Ma, Silong Yong, Zilong Zheng, Qing Li, Yitao Liang, Song-Chun Zhu, Siyuan Huang |
| 2023 | STREET: A Multi-Task Structured Reasoning and Explanation Benchmark. Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Henghui Zhu, Rui Dong, Deguang Kong, Juliette Burger, Anjelica Ramos, Zhiheng Huang, William Yang Wang, George Karypis, Bing Xiang, Dan Roth |
| 2023 | STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables. Jaehyun Nam, Jihoon Tack, Kyungmin Lee, Hankook Lee, Jinwoo Shin |
| 2023 | STaSy: Score-based Tabular data Synthesis. Jayoung Kim, Chaejeong Lee, Noseong Park |
| 2023 | SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication. Marco Bornstein, Tahseen Rabbani, Evan Wang, Amrit S. Bedi, Furong Huang |
| 2023 | SYNC: Safety-Aware Neural Control for Stabilizing Stochastic Delay-Differential Equations. Jingdong Zhang, Qunxi Zhu, Wei Yang, Wei Lin |
| 2023 | Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL. Ruiquan Huang, Jing Yang, Yingbin Liang |
| 2023 | Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation. Yannick Hogewind, Thiago D. Simão, Tal Kachman, Nils Jansen |
| 2023 | Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao |
| 2023 | Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville |
| 2023 | Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang |
| 2023 | Sampling with Mollified Interaction Energy Descent. Lingxiao Li, Qiang Liu, Anna Korba, Mikhail Yurochkin, Justin Solomon |
| 2023 | Sampling-based inference for large linear models, with application to linearised Laplace. Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato |
| 2023 | Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. Nicholas Gao, Stephan Günnemann |
| 2023 | Scaffolding a Student to Instill Knowledge. Anil Kag, Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama |
| 2023 | Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing. Renyu Zhang, Aly A. Khan, Robert L. Grossman, Yuxin Chen |
| 2023 | Scalable Subset Sampling with Neural Conditional Poisson Networks. Adeel Pervez, Phillip Lippe, Efstratios Gavves |
| 2023 | Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions. Jeremy Ocampo, Matthew A. Price, Jason D. McEwen |
| 2023 | Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel. Sungyub Kim, Sihwan Park, Kyung-Su Kim, Eunho Yang |
| 2023 | Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting. Mohammad Amin Shabani, Amir H. Abdi, Lili Meng, Tristan Sylvain |
| 2023 | Scaling Forward Gradient With Local Losses. Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey E. Hinton |
| 2023 | Scaling Laws For Deep Learning Based Image Reconstruction. Tobit Klug, Reinhard Heckel |
| 2023 | Scaling Laws for a Multi-Agent Reinforcement Learning Model. Oren Neumann, Claudius Gros |
| 2023 | Scaling Pareto-Efficient Decision Making via Offline Multi-Objective RL. Baiting Zhu, Meihua Dang, Aditya Grover |
| 2023 | Scaling Up Probabilistic Circuits by Latent Variable Distillation. Anji Liu, Honghua Zhang, Guy Van den Broeck |
| 2023 | Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation. Linfeng Zhao, Huazhe Xu, Lawson L. S. Wong |
| 2023 | Scenario-based Question Answering with Interacting Contextual Properties. Haitian Sun, William W. Cohen, Ruslan Salakhutdinov |
| 2023 | Schema Inference for Interpretable Image Classification. Haofei Zhang, Mengqi Xue, Xiaokang Liu, Kaixuan Chen, Jie Song, Mingli Song |
| 2023 | Score-based Continuous-time Discrete Diffusion Models. Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, Hanjun Dai |
| 2023 | SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation. Qiang Wan, Zilong Huang, Jiachen Lu, Gang Yu, Li Zhang |
| 2023 | Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective. Kun Wang, Yuxuan Liang, Pengkun Wang, Xu Wang, Pengfei Gu, Junfeng Fang, Yang Wang |
| 2023 | Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning. Xin-Qiang Cai, Yao-Xiang Ding, Zi-Xuan Chen, Yuan Jiang, Masashi Sugiyama, Zhi-Hua Zhou |
| 2023 | Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. Antonia Creswell, Murray Shanahan, Irina Higgins |
| 2023 | Selective Annotation Makes Language Models Better Few-Shot Learners. Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Tao Yu |
| 2023 | Selective Frequency Network for Image Restoration. Yuning Cui, Yi Tao, Zhenshan Bing, Wenqi Ren, Xinwei Gao, Xiaochun Cao, Kai Huang, Alois Knoll |
| 2023 | Self-Consistency Improves Chain of Thought Reasoning in Language Models. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou |
| 2023 | Self-Distillation for Further Pre-training of Transformers. Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi |
| 2023 | Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors. Sizhe Chen, Geng Yuan, Xinwen Cheng, Yifan Gong, Minghai Qin, Yanzhi Wang, Xiaolin Huang |
| 2023 | Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning. Jiahui Gao, Renjie Pi, Yong Lin, Hang Xu, Jiacheng Ye, Zhiyong Wu, Weizhong Zhang, Xiaodan Liang, Zhenguo Li, Lingpeng Kong |
| 2023 | Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability. Alex Damian, Eshaan Nichani, Jason D. Lee |
| 2023 | Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance. Xueyi Liu, Ji Zhang, Ruizhen Hu, Haibin Huang, He Wang, Li Yi |
| 2023 | Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild. Kaifeng Zhang, Yang Fu, Shubhankar Borse, Hong Cai, Fatih Porikli, Xiaolong Wang |
| 2023 | Self-Supervised Set Representation Learning for Unsupervised Meta-Learning. Dong Bok Lee, Seanie Lee, Kenji Kawaguchi, Yunji Kim, Jihwan Bang, Jung-Woo Ha, Sung Ju Hwang |
| 2023 | Self-supervised learning with rotation-invariant kernels. Léon Zheng, Gilles Puy, Elisa Riccietti, Patrick Pérez, Rémi Gribonval |
| 2023 | Self-supervision through Random Segments with Autoregressive Coding (RandSAC). Tianyu Hua, Yonglong Tian, Sucheng Ren, Michalis Raptis, Hang Zhao, Leonid Sigal |
| 2023 | SemPPL: Predicting Pseudo-Labels for Better Contrastive Representations. Matko Bosnjak, Pierre Harvey Richemond, Nenad Tomasev, Florian Strub, Jacob C. Walker, Felix Hill, Lars Holger Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic |
| 2023 | Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation. Lorenz Kuhn, Yarin Gal, Sebastian Farquhar |
| 2023 | Semi-Implicit Variational Inference via Score Matching. Longlin Yu, Cheng Zhang |
| 2023 | Semi-Parametric Inducing Point Networks and Neural Processes. Richa Rastogi, Yair Schiff, Alon Hacohen, Zhaozhi Li, Ian Lee, Yuntian Deng, Mert R. Sabuncu, Volodymyr Kuleshov |
| 2023 | Semi-supervised Community Detection via Structural Similarity Metrics. Yicong Jiang, Tracy Ke |
| 2023 | Semi-supervised learning with a principled likelihood from a generative model of data curation. Stoil Ganev, Laurence Aitchison |
| 2023 | Sequential Attention for Feature Selection. Taisuke Yasuda, Mohammad Hossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni |
| 2023 | Sequential Gradient Coding For Straggler Mitigation. Muralee Nikhil Krishnan, MohammadReza Ebrahimi, Ashish J. Khisti |
| 2023 | Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting. Xiajun Jiang, Ryan Missel, Zhiyuan Li, Linwei Wang |
| 2023 | Sequential Learning of Neural Networks for Prequential MDL. Jörg Bornschein, Yazhe Li, Marcus Hutter |
| 2023 | Serving Graph Compression for Graph Neural Networks. Si Si, Felix X. Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar |
| 2023 | Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning. Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, Reza Shokri |
| 2023 | Sharper Bounds for Uniformly Stable Algorithms with Stationary Mixing Process. Shi Fu, Yunwen Lei, Qiong Cao, Xinmei Tian, Dacheng Tao |
| 2023 | Short-Term Memory Convolutions. Grzegorz Stefanski, Krzysztof Arendt, Pawel Daniluk, Bartlomiej Jasik, Artur Szumaczuk |
| 2023 | Sign and Basis Invariant Networks for Spectral Graph Representation Learning. Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka |
| 2023 | SimPer: Simple Self-Supervised Learning of Periodic Targets. Yuzhe Yang, Xin Liu, Jiang Wu, Silviu Borac, Dina Katabi, Ming-Zher Poh, Daniel McDuff |
| 2023 | Simple Emergent Action Representations from Multi-Task Policy Training. Pu Hua, Yubei Chen, Huazhe Xu |
| 2023 | Simple and Scalable Nearest Neighbor Machine Translation. Yuhan Dai, Zhirui Zhang, Qiuzhi Liu, Qu Cui, Weihua Li, Yichao Du, Tong Xu |
| 2023 | Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth. Filipe de Avila Belbute-Peres, J. Zico Kolter |
| 2023 | Simplicial Embeddings in Self-Supervised Learning and Downstream Classification. Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Ankit Vani, Michael Noukhovitch, Kenji Kawaguchi, Aaron C. Courville |
| 2023 | Simplicial Hopfield networks. Thomas F. Burns, Tomoki Fukai |
| 2023 | Simplified State Space Layers for Sequence Modeling. Jimmy T. H. Smith, Andrew Warrington, Scott W. Linderman |
| 2023 | Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective. Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Russ Salakhutdinov |
| 2023 | Single-shot General Hyper-parameter Optimization for Federated Learning. Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo, Horst Samulowitz, Heiko Ludwig |
| 2023 | SketchKnitter: Vectorized Sketch Generation with Diffusion Models. Qiang Wang, Haoge Deng, Yonggang Qi, Da Li, Yi-Zhe Song |
| 2023 | SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models. Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, Animesh Garg |
| 2023 | SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing. Sheng Li, Geng Yuan, Yue Dai, Youtao Zhang, Yanzhi Wang, Xulong Tang |
| 2023 | Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language. Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Marcin Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael S. Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, Pete Florence |
| 2023 | Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning. Chongjian Ge, Jiangliu Wang, Zhan Tong, Shoufa Chen, Yibing Song, Ping Luo |
| 2023 | SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning. Hao Chen, Ran Tao, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Bhiksha Raj, Marios Savvides |
| 2023 | SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments. Tsun-Hsuan Wang, Pingchuan Ma, Andrew Everett Spielberg, Zhou Xian, Hao Zhang, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan |
| 2023 | Softened Symbol Grounding for Neuro-symbolic Systems. Zenan Li, Yuan Yao, Taolue Chen, Jingwei Xu, Chun Cao, Xiaoxing Ma, Jian Lü |
| 2023 | Solving Constrained Variational Inequalities via a First-order Interior Point-based Method. Tong Yang, Michael I. Jordan, Tatjana Chavdarova |
| 2023 | Solving Continuous Control via Q-learning. Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin A. Riedmiller, Daniela Rus, Markus Wulfmeier |
| 2023 | Solving stochastic weak Minty variational inequalities without increasing batch size. Thomas Pethick, Olivier Fercoq, Puya Latafat, Panagiotis Patrinos, Volkan Cevher |
| 2023 | Sound Randomized Smoothing in Floating-Point Arithmetic. Václav Vorácek, Matthias Hein |
| 2023 | Spacetime Representation Learning. Marc T. Law, James Lucas |
| 2023 | Sparse Distributed Memory is a Continual Learner. Trenton Bricken, Xander Davies, Deepak Singh, Dmitry Krotov, Gabriel Kreiman |
| 2023 | Sparse Mixture-of-Experts are Domain Generalizable Learners. Bo Li, Yifei Shen, Jingkang Yang, Yezhen Wang, Jiawei Ren, Tong Che, Jun Zhang, Ziwei Liu |
| 2023 | Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers. Tianlong Chen, Zhenyu Zhang, Ajay Kumar Jaiswal, Shiwei Liu, Zhangyang Wang |
| 2023 | Sparse Random Networks for Communication-Efficient Federated Learning. Berivan Isik, Francesco Pase, Deniz Gündüz, Tsachy Weissman, Michele Zorzi |
| 2023 | Sparse Token Transformer with Attention Back Tracking. Heejun Lee, Minki Kang, Youngwan Lee, Sung Ju Hwang |
| 2023 | Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints. Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, Neil Houlsby |
| 2023 | Sparse tree-based Initialization for Neural Networks. Patrick Lutz, Ludovic Arnould, Claire Boyer, Erwan Scornet |
| 2023 | Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! Shiwei Liu, Tianlong Chen, Zhenyu Zhang, Xuxi Chen, Tianjin Huang, Ajay Kumar Jaiswal, Zhangyang Wang |
| 2023 | Sparsity-Constrained Optimal Transport. Tianlin Liu, Joan Puigcerver, Mathieu Blondel |
| 2023 | Spatial Attention Kinetic Networks with E(n)-Equivariance. Yuanqing Wang, John D. Chodera |
| 2023 | Spatio-temporal point processes with deep non-stationary kernels. Zheng Dong, Xiuyuan Cheng, Yao Xie |
| 2023 | Specformer: Spectral Graph Neural Networks Meet Transformers. Deyu Bo, Chuan Shi, Lele Wang, Renjie Liao |
| 2023 | Spectral Augmentation for Self-Supervised Learning on Graphs. Lu Lin, Jinghui Chen, Hongning Wang |
| 2023 | Spectral Decomposition Representation for Reinforcement Learning. Tongzheng Ren, Tianjun Zhang, Lisa Lee, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai |
| 2023 | SpeedyZero: Mastering Atari with Limited Data and Time. Yixuan Mei, Jiaxuan Gao, Weirui Ye, Shaohuai Liu, Yang Gao, Yi Wu |
| 2023 | Spherical Sliced-Wasserstein. Clément Bonet, Paul Berg, Nicolas Courty, François Septier, Lucas Drumetz, Minh-Tan Pham |
| 2023 | Spikformer: When Spiking Neural Network Meets Transformer. Zhaokun Zhou, Yuesheng Zhu, Chao He, Yaowei Wang, Shuicheng Yan, Yonghong Tian, Li Yuan |
| 2023 | Spiking Convolutional Neural Networks for Text Classification. Changze Lv, Jianhan Xu, Xiaoqing Zheng |
| 2023 | Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus. Gang Li, Yang Li |
| 2023 | Squeeze Training for Adversarial Robustness. Qizhang Li, Yiwen Guo, Wangmeng Zuo, Hao Chen |
| 2023 | Stable Target Field for Reduced Variance Score Estimation in Diffusion Models. Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola |
| 2023 | StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random. Haoxuan Li, Chunyuan Zheng, Peng Wu |
| 2023 | Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Curtis Mozer, Nicolas Heess, Yoshua Bengio |
| 2023 | Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions. David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow |
| 2023 | Statistical Efficiency of Score Matching: The View from Isoperimetry. Frederic Koehler, Alexander Heckett, Andrej Risteski |
| 2023 | Statistical Guarantees for Consensus Clustering. Zhixin Zhou, Gautam Dudeja, Arash A. Amini |
| 2023 | Statistical Inference for Fisher Market Equilibrium. Luofeng Liao, Yuan Gao, Christian Kroer |
| 2023 | Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms. Ximing Li, Chendi Wang, Guang Cheng |
| 2023 | Stay Moral and Explore: Learn to Behave Morally in Text-based Games. Zijing Shi, Meng Fang, Yunqiu Xu, Ling Chen, Yali Du |
| 2023 | Stochastic Differentially Private and Fair Learning. Andrew Lowy, Devansh Gupta, Meisam Razaviyayn |
| 2023 | Stochastic Multi-Person 3D Motion Forecasting. Sirui Xu, Yu-Xiong Wang, Liangyan Gui |
| 2023 | Stochastic No-regret Learning for General Games with Variance Reduction. Yichi Zhou, Fang Kong, Shuai Li |
| 2023 | Strategic Classification with Graph Neural Networks. Itay Eilat, Ben Finkelshtein, Chaim Baskin, Nir Rosenfeld |
| 2023 | Strong inductive biases provably prevent harmless interpolation. Michael Aerni, Marco Milanta, Konstantin Donhauser, Fanny Yang |
| 2023 | StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training. Yuechen Yu, Yulin Li, Chengquan Zhang, Xiaoqiang Zhang, Zengyuan Guo, Xiameng Qin, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang |
| 2023 | Structure by Architecture: Structured Representations without Regularization. Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf |
| 2023 | StyleMorph: Disentangled 3D-Aware Image Synthesis with a 3D Morphable StyleGAN. Eric-Tuan Le, Edward Bartrum, Iasonas Kokkinos |
| 2023 | Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks. Noam Wies, Yoav Levine, Amnon Shashua |
| 2023 | Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation. Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou |
| 2023 | Subsampling in Large Graphs Using Ricci Curvature. Shushan Wu, Huimin Cheng, Jiazhang Cai, Ping Ma, Wenxuan Zhong |
| 2023 | Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees. Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal |
| 2023 | Supervision Complexity and its Role in Knowledge Distillation. Hrayr Harutyunyan, Ankit Singh Rawat, Aditya Krishna Menon, Seungyeon Kim, Sanjiv Kumar |
| 2023 | Suppressing the Heterogeneity: A Strong Feature Extractor for Few-shot Segmentation. Zhengdong Hu, Yifan Sun, Yi Yang |
| 2023 | Surgical Fine-Tuning Improves Adaptation to Distribution Shifts. Yoonho Lee, Annie S. Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, Chelsea Finn |
| 2023 | Switch-NeRF: Learning Scene Decomposition with Mixture of Experts for Large-scale Neural Radiance Fields. Zhenxing Mi, Dan Xu |
| 2023 | Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search. Fangzheng Sun, Yang Liu, Jian-Xun Wang, Hao Sun |
| 2023 | Symmetric Pruning in Quantum Neural Networks. Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao |
| 2023 | Symmetries, Flat Minima, and the Conserved Quantities of Gradient Flow. Bo Zhao, Iordan Ganev, Robin Walters, Rose Yu, Nima Dehmamy |
| 2023 | Synthetic Data Generation of Many-to-Many Datasets via Random Graph Generation. Kai Xu, Georgi Ganev, Emile Joubert, Rees Davison, Olivier Van Acker, Luke Robinson |
| 2023 | Systematic Rectification of Language Models via Dead-end Analysis. Meng Cao, Mehdi Fatemi, Jackie C. K. Cheung, Samira Shabanian |
| 2023 | TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar |
| 2023 | TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations. Haoxuan Li, Yan Lyu, Chunyuan Zheng, Peng Wu |
| 2023 | TEMPERA: Test-Time Prompt Editing via Reinforcement Learning. Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, Joseph E. Gonzalez |
| 2023 | TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs. Siheng Xiong, Yuan Yang, Faramarz Fekri, James Clayton Kerce |
| 2023 | TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation. Hyesu Lim, Byeonggeun Kim, Jaegul Choo, Sungha Choi |
| 2023 | TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning. Chaitanya Murti, Tanay Narshana, Chiranjib Bhattacharyya |
| 2023 | TabCaps: A Capsule Neural Network for Tabular Data Classification with BoW Routing. Jintai Chen, Kuanlun Liao, Yanwen Fang, Danny Z. Chen, Jian Wu |
| 2023 | TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter |
| 2023 | Tailoring Language Generation Models under Total Variation Distance. Haozhe Ji, Pei Ke, Zhipeng Hu, Rongsheng Zhang, Minlie Huang |
| 2023 | Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. Zhen Lin, Shubhendu Trivedi, Jimeng Sun |
| 2023 | Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives. Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu |
| 2023 | Task Ambiguity in Humans and Language Models. Alex Tamkin, Kunal Handa, Avash Shrestha, Noah D. Goodman |
| 2023 | Task-Aware Information Routing from Common Representation Space in Lifelong Learning. Prashant Shivaram Bhat, Bahram Zonooz, Elahe Arani |
| 2023 | Task-customized Masked Autoencoder via Mixture of Cluster-conditional Experts. Zhili Liu, Kai Chen, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, James T. Kwok |
| 2023 | TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene Understanding. Hanrong Ye, Dan Xu |
| 2023 | Teacher Guided Training: An Efficient Framework for Knowledge Transfer. Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar |
| 2023 | TempCLR: Temporal Alignment Representation with Contrastive Learning. Yuncong Yang, Jiawei Ma, Shiyuan Huang, Long Chen, Xudong Lin, Guangxing Han, Shih-Fu Chang |
| 2023 | Temperature Schedules for self-supervised contrastive methods on long-tail data. Anna Kukleva, Moritz Böhle, Bernt Schiele, Hilde Kuehne, Christian Rupprecht |
| 2023 | Temporal Coherent Test Time Optimization for Robust Video Classification. Chenyu Yi, Siyuan Yang, Yufei Wang, Haoliang Li, Yap-Peng Tan, Alex C. Kot |
| 2023 | Temporal Dependencies in Feature Importance for Time Series Prediction. Kin Kwan Leung, Clayton Rooke, Jonathan Smith, Saba Zuberi, Maksims Volkovs |
| 2023 | Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning. Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah P. Hanna, Stefano V. Albrecht |
| 2023 | Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks. Guangji Bai, Chen Ling, Liang Zhao |
| 2023 | Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams. Cuiyu Liu, Chuanfu Xiao, Mingshuo Ding, Chao Yang |
| 2023 | Test-Time Adaptation via Self-Training with Nearest Neighbor Information. Minguk Jang, Sae-Young Chung, Hye Won Chung |
| 2023 | Test-Time Robust Personalization for Federated Learning. Liangze Jiang, Tao Lin |
| 2023 | Text Summarization with Oracle Expectation. Yumo Xu, Mirella Lapata |
| 2023 | TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization. Bairu Hou, Jinghan Jia, Yihua Zhang, Guanhua Zhang, Yang Zhang, Sijia Liu, Shiyu Chang |
| 2023 | TextShield: Beyond Successfully Detecting Adversarial Sentences in text classification. Lingfeng Shen, Ze Zhang, Haiyun Jiang, Ying Chen |
| 2023 | Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations. Ali Hummos |
| 2023 | That Label's got Style: Handling Label Style Bias for Uncertain Image Segmentation. Kilian Zepf, Eike Petersen, Jes Frellsen, Aasa Feragen |
| 2023 | The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks. Daniel Kunin, Atsushi Yamamura, Chao Ma, Surya Ganguli |
| 2023 | The Augmented Image Prior: Distilling 1000 Classes by Extrapolating from a Single Image. Yuki M. Asano, Aaqib Saeed |
| 2023 | The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation. Huancheng Chen, Chianing Wang, Haris Vikalo |
| 2023 | The Curious Case of Benign Memorization. Sotiris Anagnostidis, Gregor Bachmann, Lorenzo Noci, Thomas Hofmann |
| 2023 | The Dark Side of AutoML: Towards Architectural Backdoor Search. Ren Pang, Changjiang Li, Zhaohan Xi, Shouling Ji, Ting Wang |
| 2023 | The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition. Jun Cen, Di Luan, Shiwei Zhang, Yixuan Pei, Yingya Zhang, Deli Zhao, Shaojie Shen, Qifeng Chen |
| 2023 | The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 |
| 2023 | The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks. Mor Shpigel Nacson, Rotem Mulayoff, Greg Ongie, Tomer Michaeli, Daniel Soudry |
| 2023 | The In-Sample Softmax for Offline Reinforcement Learning. Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White |
| 2023 | The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks. Blake Bordelon, Cengiz Pehlevan |
| 2023 | The KFIoU Loss for Rotated Object Detection. Xue Yang, Yue Zhou, Gefan Zhang, Jirui Yang, Wentao Wang, Junchi Yan, Xiaopeng Zhang, Qi Tian |
| 2023 | The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers. Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li, Ankit Singh Rawat, Sashank J. Reddi, Ke Ye, Felix Chern, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar |
| 2023 | The Lie Derivative for Measuring Learned Equivariance. Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson |
| 2023 | The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation. Zihui Xue, Zhengqi Gao, Sucheng Ren, Hang Zhao |
| 2023 | The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. Alexander B. Atanasov, Blake Bordelon, Sabarish Sainathan, Cengiz Pehlevan |
| 2023 | The Power of Regularization in Solving Extensive-Form Games. Mingyang Liu, Asuman E. Ozdaglar, Tiancheng Yu, Kaiqing Zhang |
| 2023 | The Provable Benefit of Unsupervised Data Sharing for Offline Reinforcement Learning. Hao Hu, Yiqin Yang, Qianchuan Zhao, Chongjie Zhang |
| 2023 | The Role of Coverage in Online Reinforcement Learning. Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade |
| 2023 | The Role of ImageNet Classes in Fréchet Inception Distance. Tuomas Kynkäänniemi, Tero Karras, Miika Aittala, Timo Aila, Jaakko Lehtinen |
| 2023 | The Surprising Computational Power of Nondeterministic Stack RNNs. Brian DuSell, David Chiang |
| 2023 | The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry. Dian Wang, Jung Yeon Park, Neel Sortur, Lawson L. S. Wong, Robin Walters, Robert Platt |
| 2023 | The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium. Ian Gemp, Charlie Chen, Brian McWilliams |
| 2023 | The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection. Griffin Floto, Stefan Kremer, Mihai Nica |
| 2023 | The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning. Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha |
| 2023 | The hidden uniform cluster prior in self-supervised learning. Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Nicolas Ballas |
| 2023 | Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning. Peizhong Ju, Yingbin Liang, Ness B. Shroff |
| 2023 | This Looks Like It Rather Than That: ProtoKNN For Similarity-Based Classifiers. Yuki Ukai, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi |
| 2023 | TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization. Xiang Li, Junchi Yang, Niao He |
| 2023 | Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors. Zeyu Tang, Yatong Chen, Yang Liu, Kun Zhang |
| 2023 | Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection. Jinhyung Park, Chenfeng Xu, Shijia Yang, Kurt Keutzer, Kris M. Kitani, Masayoshi Tomizuka, Wei Zhan |
| 2023 | Time to augment self-supervised visual representation learning. Arthur Aubret, Markus Roland Ernst, Céline Teulière, Jochen Triesch |
| 2023 | TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, Mingsheng Long |
| 2023 | Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints. David Henry Mguni, Aivar Sootla, Juliusz Ziomek, Oliver Slumbers, Zipeng Dai, Kun Shao, Jun Wang |
| 2023 | Toeplitz Neural Network for Sequence Modeling. Zhen Qin, Xiaodong Han, Weixuan Sun, Bowen He, Dong Li, Dongxu Li, Yuchao Dai, Lingpeng Kong, Yiran Zhong |
| 2023 | Token Merging: Your ViT But Faster. Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, Judy Hoffman |
| 2023 | Topology-aware Robust Optimization for Out-of-Distribution Generalization. Fengchun Qiao, Xi Peng |
| 2023 | Toward Adversarial Training on Contextualized Language Representation. Hongqiu Wu, Yongxiang Liu, Hanwen Shi, Hai Zhao, Min Zhang |
| 2023 | Towards Addressing Label Skews in One-Shot Federated Learning. Yiqun Diao, Qinbin Li, Bingsheng He |
| 2023 | Towards Better Selective Classification. Leo Feng, Mohamed Osama Ahmed, Hossein Hajimirsadeghi, Amir H. Abdi |
| 2023 | Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective. Yiming Gao, Feiyu Liu, Liang Wang, Zhenjie Lian, Weixuan Wang, Siqin Li, Xianliang Wang, Xianhan Zeng, Rundong Wang, Jiawei Wang, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu |
| 2023 | Towards Inferential Reproducibility of Machine Learning Research. Michael Hagmann, Philipp Meier, Stefan Riezler |
| 2023 | Towards Interpretable Deep Reinforcement Learning with Human-Friendly Prototypes. Eoin M. Kenny, Mycal Tucker, Julie Shah |
| 2023 | Towards Lightweight, Model-Agnostic and Diversity-Aware Active Anomaly Detection. Xu Zhang, Yuan Zhao, Ziang Cui, Liqun Li, Shilin He, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang |
| 2023 | Towards Minimax Optimal Reward-free Reinforcement Learning in Linear MDPs. Pihe Hu, Yu Chen, Longbo Huang |
| 2023 | Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case. Runzhong Wang, Li Shen, Yiting Chen, Xiaokang Yang, Dacheng Tao, Junchi Yan |
| 2023 | Towards Open Temporal Graph Neural Networks. Kaituo Feng, Changsheng Li, Xiaolu Zhang, Jun Zhou |
| 2023 | Towards Robust Object Detection Invariant to Real-World Domain Shifts. Qi Fan, Mattia Segù, Yu-Wing Tai, Fisher Yu, Chi-Keung Tang, Bernt Schiele, Dengxin Dai |
| 2023 | Towards Robustness Certification Against Universal Perturbations. Yi Zeng, Zhouxing Shi, Ming Jin, Feiyang Kang, Lingjuan Lyu, Cho-Jui Hsieh, Ruoxi Jia |
| 2023 | Towards Smooth Video Composition. Qihang Zhang, Ceyuan Yang, Yujun Shen, Yinghao Xu, Bolei Zhou |
| 2023 | Towards Stable Test-time Adaptation in Dynamic Wild World. Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, Mingkui Tan |
| 2023 | Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning. Zeyuan Allen-Zhu, Yuanzhi Li |
| 2023 | Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation. Jun-Kun Wang, Andre Wibisono |
| 2023 | Towards Understanding Why Mask Reconstruction Pretraining Helps in Downstream Tasks. Jiachun Pan, Pan Zhou, Shuicheng Yan |
| 2023 | Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning. Yujun Shi, Jian Liang, Wenqing Zhang, Vincent Y. F. Tan, Song Bai |
| 2023 | Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism. Zhijian Zhuo, Yifei Wang, Jinwen Ma, Yisen Wang |
| 2023 | Towards convergence to Nash equilibria in two-team zero-sum games. Fivos Kalogiannis, Ioannis Panageas, Emmanouil V. Vlatakis-Gkaragkounis |
| 2023 | Towards the Generalization of Contrastive Self-Supervised Learning. Weiran Huang, Mingyang Yi, Xuyang Zhao, Zihao Jiang |
| 2023 | Trading Information between Latents in Hierarchical Variational Autoencoders. Tim Z. Xiao, Robert Bamler |
| 2023 | Trainability Preserving Neural Pruning. Huan Wang, Yun Fu |
| 2023 | Trainable Weight Averaging: Efficient Training by Optimizing Historical Solutions. Tao Li, Zhehao Huang, Qinghua Tao, Yingwen Wu, Xiaolin Huang |
| 2023 | Training language models to summarize narratives improves brain alignment. Khai Loong Aw, Mariya Toneva |
| 2023 | Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis. Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun R. Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, William Yang Wang |
| 2023 | TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation. Rongjie Huang, Jinglin Liu, Huadai Liu, Yi Ren, Lichao Zhang, Jinzheng He, Zhou Zhao |
| 2023 | Transfer Learning with Deep Tabular Models. Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum |
| 2023 | Transfer NAS with Meta-learned Bayesian Surrogates. Gresa Shala, Thomas Elsken, Frank Hutter, Josif Grabocka |
| 2023 | Transferable Unlearnable Examples. Jie Ren, Han Xu, Yuxuan Wan, Xingjun Ma, Lichao Sun, Jiliang Tang |
| 2023 | Transformer Meets Boundary Value Inverse Problems. Ruchi Guo, Shuhao Cao, Long Chen |
| 2023 | Transformer-Patcher: One Mistake Worth One Neuron. Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, Zhang Xiong |
| 2023 | Transformer-based World Models Are Happy With 100k Interactions. Jan Robine, Marc Höftmann, Tobias Uelwer, Stefan Harmeling |
| 2023 | Transformer-based model for symbolic regression via joint supervised learning. Wenqiang Li, Weijun Li, Linjun Sun, Min Wu, Lina Yu, Jingyi Liu, Yanjie Li, Songsong Tian |
| 2023 | Transformers Learn Shortcuts to Automata. Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang |
| 2023 | Transformers are Sample-Efficient World Models. Vincent Micheli, Eloi Alonso, François Fleuret |
| 2023 | Treeformer: Dense Gradient Trees for Efficient Attention Computation. Lovish Madaan, Srinadh Bhojanapalli, Himanshu Jain, Prateek Jain |
| 2023 | TrojText: Test-time Invisible Textual Trojan Insertion. Qian Lou, Yepeng Liu, Bo Feng |
| 2023 | Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders. Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou |
| 2023 | Truthful Self-Play. Shohei Ohsawa |
| 2023 | Tuning Frequency Bias in Neural Network Training with Nonuniform Data. Annan Yu, Yunan Yang, Alex Townsend |
| 2023 | Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection. Shuyang Yu, Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou |
| 2023 | TypeT5: Seq2seq Type Inference using Static Analysis. Jiayi Wei, Greg Durrett, Isil Dillig |
| 2023 | UL2: Unifying Language Learning Paradigms. Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Denny Zhou, Neil Houlsby, Donald Metzler |
| 2023 | UNICORN: A Unified Backdoor Trigger Inversion Framework. Zhenting Wang, Kai Mei, Juan Zhai, Shiqing Ma |
| 2023 | UNIFIED-IO: A Unified Model for Vision, Language, and Multi-modal Tasks. Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, Aniruddha Kembhavi |
| 2023 | Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States. Mingjie Li, Yifei Wang, Yisen Wang, Zhouchen Lin |
| 2023 | Unbiased Supervised Contrastive Learning. Carlo Alberto Barbano, Benoit Dufumier, Enzo Tartaglione, Marco Grangetto, Pietro Gori |
| 2023 | Understanding DDPM Latent Codes Through Optimal Transport. Valentin Khrulkov, Gleb V. Ryzhakov, Andrei Chertkov, Ivan V. Oseledets |
| 2023 | Understanding Edge-of-Stability Training Dynamics with a Minimalist Example. Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge |
| 2023 | Understanding Embodied Reference with Touch-Line Transformer. Yang Li, Xiaoxue Chen, Hao Zhao, Jiangtao Gong, Guyue Zhou, Federico Rossano, Yixin Zhu |
| 2023 | Understanding Influence Functions and Datamodels via Harmonic Analysis. Nikunj Saunshi, Arushi Gupta, Mark Braverman, Sanjeev Arora |
| 2023 | Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. Martin Bjerke, Lukas Schott, Kristopher T. Jensen, Claudia Battistin, David A. Klindt, Benjamin Adric Dunn |
| 2023 | Understanding The Robustness of Self-supervised Learning Through Topic Modeling. Zeping Luo, Shiyou Wu, Cindy Weng, Mo Zhou, Rong Ge |
| 2023 | Understanding Train-Validation Split in Meta-Learning with Neural Networks. Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu |
| 2023 | Understanding Why Generalized Reweighting Does Not Improve Over ERM. Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Kumar Ravikumar |
| 2023 | Understanding Zero-shot Adversarial Robustness for Large-Scale Models. Chengzhi Mao, Scott Geng, Junfeng Yang, Xin Wang, Carl Vondrick |
| 2023 | Understanding and Adopting Rational Behavior by Bellman Score Estimation. Kuno Kim, Stefano Ermon |
| 2023 | Understanding new tasks through the lens of training data via exponential tilting. Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun |
| 2023 | Understanding the Covariance Structure of Convolutional Filters. Asher Trockman, Devin Willmott, J. Zico Kolter |
| 2023 | Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu |
| 2023 | Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning. Yuandong Tian |
| 2023 | Understanding weight-magnitude hyperparameters in training binary networks. Joris Quist, Yunqiang Li, Jan van Gemert |
| 2023 | Uni-Mol: A Universal 3D Molecular Representation Learning Framework. Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke |
| 2023 | UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph. Jinhao Jiang, Kun Zhou, Xin Zhao, Ji-Rong Wen |
| 2023 | UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining. Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant |
| 2023 | Unicom: Universal and Compact Representation Learning for Image Retrieval. Xiang An, Jiankang Deng, Kaicheng Yang, Jaiwei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu |
| 2023 | Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization. Zonghan Yang, Xiaoyuan Yi, Peng Li, Yang Liu, Xing Xie |
| 2023 | Unified Discrete Diffusion for Simultaneous Vision-Language Generation. Minghui Hu, Chuanxia Zheng, Zuopeng Yang, Tat-Jen Cham, Heliang Zheng, Chaoyue Wang, Dacheng Tao, Ponnuthurai N. Suganthan |
| 2023 | Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics. Taiji Suzuki, Atsushi Nitanda, Denny Wu |
| 2023 | Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching. Donggyun Kim, Jinwoo Kim, Seongwoong Cho, Chong Luo, Seunghoon Hong |
| 2023 | Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval. Zhenghao Liu, Chenyan Xiong, Yuanhuiyi Lv, Zhiyuan Liu, Ge Yu |
| 2023 | Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite |
| 2023 | Unsupervised 3D Object Learning through Neuron Activity aware Plasticity. Beomseok Kang, Biswadeep Chakraborty, Saibal Mukhopadhyay |
| 2023 | Unsupervised Learning for Combinatorial Optimization Needs Meta Learning. Haoyu Peter Wang, Pan Li |
| 2023 | Unsupervised Manifold Alignment with Joint Multidimensional Scaling. Dexiong Chen, Bowen Fan, Carlos G. Oliver, Karsten M. Borgwardt |
| 2023 | Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning. Huiwon Jang, Hankook Lee, Jinwoo Shin |
| 2023 | Unsupervised Model Selection for Time Series Anomaly Detection. Mononito Goswami, Cristian I. Challu, Laurent Callot, Lenon Minorics, Andrey Kan |
| 2023 | Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. Andrii Zadaianchuk, Matthäus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox |
| 2023 | Unsupervised visualization of image datasets using contrastive learning. Jan Niklas Böhm, Philipp Berens, Dmitry Kobak |
| 2023 | Unveiling the sampling density in non-uniform geometric graphs. Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie |
| 2023 | User-Interactive Offline Reinforcement Learning. Phillip Swazinna, Steffen Udluft, Thomas A. Runkler |
| 2023 | Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks. Albert Yu, Raymond J. Mooney |
| 2023 | Using Language to Extend to Unseen Domains. Lisa Dunlap, Clara Mohri, Devin Guillory, Han Zhang, Trevor Darrell, Joseph E. Gonzalez, Aditi Raghunathan, Anna Rohrbach |
| 2023 | VA-DepthNet: A Variational Approach to Single Image Depth Prediction. Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Luc Van Gool |
| 2023 | VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training. Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, Amy Zhang |
| 2023 | VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation. Thanh Nguyen-Tang, Raman Arora |
| 2023 | Valid P-Value for Deep Learning-driven Salient Region. Daiki Miwa, Vo Nguyen Le Duy, Ichiro Takeuchi |
| 2023 | Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning. Deyao Zhu, Li Erran Li, Mohamed Elhoseiny |
| 2023 | Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. Eduard Gorbunov, Samuel Horváth, Peter Richtárik, Gauthier Gidel |
| 2023 | Variance-Aware Sparse Linear Bandits. Yan Dai, Ruosong Wang, Simon Shaolei Du |
| 2023 | Variational Information Pursuit for Interpretable Predictions. Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin David Haeffele, Donald Geman, René Vidal |
| 2023 | Variational Latent Branching Model for Off-Policy Evaluation. Qitong Gao, Ge Gao, Min Chi, Miroslav Pajic |
| 2023 | Verifying the Union of Manifolds Hypothesis for Image Data. Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem |
| 2023 | Versatile Neural Processes for Learning Implicit Neural Representations. Zongyu Guo, Cuiling Lan, Zhizheng Zhang, Yan Lu, Zhibo Chen |
| 2023 | Video Scene Graph Generation from Single-Frame Weak Supervision. Siqi Chen, Jun Xiao, Long Chen |
| 2023 | View Synthesis with Sculpted Neural Points. Yiming Zuo, Jia Deng |
| 2023 | ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency. Pengzhen Ren, Changlin Li, Hang Xu, Yi Zhu, Guangrun Wang, Jianzhuang Liu, Xiaojun Chang, Xiaodan Liang |
| 2023 | Vision Transformer Adapter for Dense Predictions. Zhe Chen, Yuchen Duan, Wenhai Wang, Junjun He, Tong Lu, Jifeng Dai, Yu Qiao |
| 2023 | Visual Classification via Description from Large Language Models. Sachit Menon, Carl Vondrick |
| 2023 | Visual Imitation Learning with Patch Rewards. Minghuan Liu, Tairan He, Weinan Zhang, Shuicheng Yan, Zhongwen Xu |
| 2023 | Visual Recognition with Deep Nearest Centroids. Wenguan Wang, Cheng Han, Tianfei Zhou, Dongfang Liu |
| 2023 | Visually-Augmented Language Modeling. Weizhi Wang, Li Dong, Hao Cheng, Haoyu Song, Xiaodong Liu, Xifeng Yan, Jianfeng Gao, Furu Wei |
| 2023 | VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis. Angtian Wang, Peng Wang, Jian Sun, Adam Kortylewski, Alan L. Yuille |
| 2023 | Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding. Abdullah Hamdi, Silvio Giancola, Bernard Ghanem |
| 2023 | Volumetric Optimal Transportation by Fast Fourier Transform. Na Lei, Dongsheng An, Min Zhang, Xiaoyin Xu, Xianfeng David Gu |
| 2023 | Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction. Tong Wu, Jiaqi Wang, Xingang Pan, Xudong Xu, Christian Theobalt, Ziwei Liu, Dahua Lin |
| 2023 | Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning. Do-Yeon Kim, Dong-Jun Han, Jun Seo, Jaekyun Moon |
| 2023 | Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees. Florent Delgrange, Ann Nowé, Guillermo A. Pérez |
| 2023 | Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic. Zijun Wu, Zi Xuan Zhang, Atharva Naik, Zhijian Mei, Mauajama Firdaus, Lili Mou |
| 2023 | Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection. Martijn Oldenhof, Adam Arany, Yves Moreau, Edward De Brouwer |
| 2023 | Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning. Bo Wan, Yongfei Liu, Desen Zhou, Tinne Tuytelaars, Xuming He |
| 2023 | Weighted Clock Logic Point Process. Ruixuan Yan, Yunshi Wen, Debarun Bhattacharjya, Ronny Luss, Tengfei Ma, Achille Fokoue, Anak Agung Julius |
| 2023 | Weighted Ensemble Self-Supervised Learning. Yangjun Ruan, Saurabh Singh, Warren Richard Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon |
| 2023 | What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? Ido Galil, Mohammed Dabbah, Ran El-Yaniv |
| 2023 | What Do Self-Supervised Vision Transformers Learn? Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, Sangdoo Yun |
| 2023 | What Is Missing in IRM Training and Evaluation? Challenges and Solutions. Yihua Zhang, Pranay Sharma, Parikshit Ram, Mingyi Hong, Kush R. Varshney, Sijia Liu |
| 2023 | What Makes Convolutional Models Great on Long Sequence Modeling? Yuhong Li, Tianle Cai, Yi Zhang, Deming Chen, Debadeepta Dey |
| 2023 | What learning algorithm is in-context learning? Investigations with linear models. Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, Denny Zhou |
| 2023 | What shapes the loss landscape of self supervised learning? Liu Ziyin, Ekdeep Singh Lubana, Masahito Ueda, Hidenori Tanaka |
| 2023 | When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning. Jianxiong Li, Xianyuan Zhan, Haoran Xu, Xiangyu Zhu, Jingjing Liu, Ya-Qin Zhang |
| 2023 | When Source-Free Domain Adaptation Meets Learning with Noisy Labels. Li Yi, Gezheng Xu, Pengcheng Xu, Jiaqi Li, Ruizhi Pu, Charles Ling, A. Ian McLeod, Boyu Wang |
| 2023 | When and Why Vision-Language Models Behave like Bags-Of-Words, and What to Do About It? Mert Yüksekgönül, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, James Zou |
| 2023 | When to Make and Break Commitments? Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar |
| 2023 | Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi, Michael G. Rabbat |
| 2023 | Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. Raghav Singhal, Mark Goldstein, Rajesh Ranganath |
| 2023 | Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks. Yixiong Chen, Alan L. Yuille, Zongwei Zhou |
| 2023 | Why (and When) does Local SGD Generalize Better than SGD? Xinran Gu, Kaifeng Lyu, Longbo Huang, Sanjeev Arora |
| 2023 | Why adversarial training can hurt robust accuracy. Jacob Clarysse, Julia Hörrmann, Fanny Yang |
| 2023 | WiNeRT: Towards Neural Ray Tracing for Wireless Channel Modelling and Differentiable Simulations. Tribhuvanesh Orekondy, Kumar Pratik, Shreya Kadambi, Hao Ye, Joseph Soriaga, Arash Behboodi |
| 2023 | WikiWhy: Answering and Explaining Cause-and-Effect Questions. Matthew Ho, Aditya Sharma, Justin Chang, Michael Saxon, Sharon Levy, Yujie Lu, William Yang Wang |
| 2023 | Win: Weight-Decay-Integrated Nesterov Acceleration for Adaptive Gradient Algorithms. Pan Zhou, Xingyu Xie, Shuicheng Yan |
| 2023 | Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic. Yulhwa Kim, Jaeyong Jang, Jehun Lee, Jihoon Park, Jeonghoon Kim, Byeongwook Kim, Baeseong Park, Se Jung Kwon, Dongsoo Lee, Jae-Joon Kim |
| 2023 | Words are all you need? Language as an approximation for human similarity judgments. Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby |
| 2023 | Write and Paint: Generative Vision-Language Models are Unified Modal Learners. Shizhe Diao, Wangchunshu Zhou, Xinsong Zhang, Jiawei Wang |
| 2023 | Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding. Tianyang Hu, Zhili Liu, Fengwei Zhou, Wenjia Wang, Weiran Huang |
| 2023 | Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model. Yinhuai Wang, Jiwen Yu, Jian Zhang |
| 2023 | Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation. Yao Shu, Zhongxiang Dai, Weicong Sng, Arun Verma, Patrick Jaillet, Bryan Kian Hsiang Low |
| 2023 | ZiCo: Zero-shot NAS via inverse Coefficient of Variation on Gradients. Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, Radu Marculescu |
| 2023 | f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation. Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Miguel Ángel Bautista, Joshua M. Susskind |
| 2023 | gDDIM: Generalized denoising diffusion implicit models. Qinsheng Zhang, Molei Tao, Yongxin Chen |
| 2023 | kNN-Diffusion: Image Generation via Large-Scale Retrieval. Shelly Sheynin, Oron Ashual, Adam Polyak, Uriel Singer, Oran Gafni, Eliya Nachmani, Yaniv Taigman |
| 2023 | simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing. Zitao Liu, Qiongqiong Liu, Jiahao Chen, Shuyan Huang, Weiqi Luo |
| 2023 | wav2tok: Deep Sequence Tokenizer for Audio Retrieval. Adhiraj Banerjee, Vipul Arora |
| 2023 | 𝒩-WL: A New Hierarchy of Expressivity for Graph Neural Networks. Qing Wang, Dillon Ze Chen, Asiri Wijesinghe, Shouheng Li, Muhammad Farhan |
| 2023 | 𝒪-GNN: incorporating ring priors into molecular modeling. Jinhua Zhu, Kehan Wu, Bohan Wang, Yingce Xia, Shufang Xie, Qi Meng, Lijun Wu, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu |