| 2023 | A Bayesian approach for bandit online optimization with switching cost. Zai Shi, Jian Tan, Feifei Li |
| 2023 | A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models. Sinong Geng, Houssam Nassif, Carlos A. Manzanares |
| 2023 | A constrained Bayesian approach to out-of-distribution prediction. Ziyu Wang, Binjie Yuan, Jiaxun Lu, Bowen Ding, Yunfeng Shao, Qibin Wu, Jun Zhu |
| 2023 | A decoder suffices for query-adaptive variational inference. Sakshi Agarwal, Gabriel Hope, Ali Younis, Erik B. Sudderth |
| 2023 | A near-optimal high-probability swap-Regret upper bound for multi-agent bandits in unknown general-sum games. Zhiming Huang, Jianping Pan |
| 2023 | A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization. Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian, Saeed Ghadimi |
| 2023 | A policy gradient approach for optimization of smooth risk measures. Nithia Vijayan, Prashanth L. A. |
| 2023 | A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks. Ali Gorji, Andisheh Amrollahi, Andreas Krause |
| 2023 | A trajectory is worth three sentences: multimodal transformer for offline reinforcement learning. Yiqi Wang, Mengdi Xu, Laixi Shi, Yuejie Chi |
| 2023 | ASTRA: Understanding the practical impact of robustness for probabilistic programs. Zixin Huang, Saikat Dutta, Sasa Misailovic |
| 2023 | AUC Maximization in Imbalanced Lifelong Learning. Xiangyu Zhu, Jie Hao, Yunhui Guo, Mingrui Liu |
| 2023 | Accelerating Voting by Quantum Computation. Ao Liu, Qishen Han, Lirong Xia, Nengkun Yu |
| 2023 | Active metric learning and classification using similarity queries. Namrata Nadagouda, Austin Xu, Mark A. Davenport |
| 2023 | Adaptive Conditional Quantile Neural Processes. Peiman Mohseni, Nick Duffield, Bani K. Mallick, Arman Hasanzadeh |
| 2023 | Adaptivity Complexity for Causal Graph Discovery. Davin Choo, Kirankumar Shiragur |
| 2023 | Aligned Diffusion Schrödinger Bridges. Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, María Rodríguez Martínez, Andreas Krause, Charlotte Bunne |
| 2023 | Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels. Matthias Bitzer, Mona Meister, Christoph Zimmer |
| 2023 | An effective negotiating agent framework based on deep offline reinforcement learning. Siqi Chen, Jianing Zhao, Gerhard Weiss, Ran Su, Kaiyou Lei |
| 2023 | An improved variational approximate posterior for the deep Wishart process. Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison |
| 2023 | Approximate Thompson Sampling via Epistemic Neural Networks. Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy |
| 2023 | Approximately Bayes-optimal pseudo-label selection. Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin |
| 2023 | Approximating probabilistic explanations via supermodular minimization. Louenas Bounia, Frédéric Koriche |
| 2023 | Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions. Karine Karine, Predrag V. Klasnja, Susan A. Murphy, Benjamin M. Marlin |
| 2023 | BISCUIT: Causal Representation Learning from Binary Interactions. Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves |
| 2023 | Bandits with costly reward observations. Aaron David Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims |
| 2023 | Bayesian inference approach for entropy regularized reinforcement learning with stochastic dynamics. Argenis Arriojas, Jacob Adamczyk, Stas Tiomkin, Rahul V. Kulkarni |
| 2023 | Bayesian inference for vertex-series-parallel partial orders. Chuxuan Jiang, Geoff K. Nicholls, Jeong-Eun (Kate) Lee |
| 2023 | Baysian numerical integration with neural networks. Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol |
| 2023 | BeliefPPG: Uncertainty-aware heart rate estimation from PPG signals via belief propagation. Valentin Bieri, Paul Streli, Berken Utku Demirel, Christian Holz |
| 2023 | Benefits of monotonicity in safe exploration with Gaussian processes. Arpan Losalka, Jonathan Scarlett |
| 2023 | Benign Overfitting in Adversarially Robust Linear Classification. Jinghui Chen, Yuan Cao, Quanquan Gu |
| 2023 | Best arm identification in rare events. Anirban Bhattacharjee, Sushant Vijayan, Sandeep Juneja |
| 2023 | Bidirectional Attention as a Mixture of Continuous Word Experts. Kevin Christian Wibisono, Yixin Wang |
| 2023 | Birds of an odd feather: guaranteed out-of-distribution (OOD) novel category detection. Yoav Wald, Suchi Saria |
| 2023 | Blackbox optimization of unimodal functions. Ashok Cutkosky, Abhimanyu Das, Weihao Kong, Chansoo Lee, Rajat Sen |
| 2023 | Boosting AND/OR-based computational protein design: dynamic heuristics and generalizable UFO. Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter |
| 2023 | Bounding the optimal value function in compositional reinforcement learning. Jacob Adamczyk, Volodymyr Makarenko, Argenis Arriojas, Stas Tiomkin, Rahul V. Kulkarni |
| 2023 | CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models. Jiazheng Li, Zhaoyue Sun, Bin Liang, Lin Gui, Yulan He |
| 2023 | Causal Discovery for time series from multiple datasets with latent contexts. Wiebke Günther, Urmi Ninad, Jakob Runge |
| 2023 | Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition. David Kaltenpoth, Jilles Vreeken |
| 2023 | Causal effect estimation from observational and interventional data through matrix weighted linear estimators. Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach |
| 2023 | Causal inference with outcome-dependent missingness and self-censoring. Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya |
| 2023 | Causal information splitting: Engineering proxy features for robustness to distribution shifts. Bijan Mazaheri, Atalanti-Anastasia Mastakouri, Dominik Janzing, Michaela Hardt |
| 2023 | Combinatorial categorized bandits with expert rankings. Sayak Ray Chowdhury, Gaurav Sinha, Nagarajan Natarajan, Amit Sharma |
| 2023 | Composing Efficient, Robust Tests for Policy Selection. Dustin Morrill, Thomas J. Walsh, Daniel Hernandez, Peter R. Wurman, Peter Stone |
| 2023 | Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow. Denis A. Gudovskiy, Tomoyuki Okuno, Yohei Nakata |
| 2023 | Conditional abstraction trees for sample-efficient reinforcement learning. Mehdi Dadvar, Rashmeet Kaur Nayyar, Siddharth Srivastava |
| 2023 | Conditional counterfactual causal effect for individual attribution. Ruiqi Zhao, Lei Zhang, Shengyu Zhu, Zitong Lu, Zhenhua Dong, Chaoliang Zhang, Jun Xu, Zhi Geng, Yangbo He |
| 2023 | Conditionally optimistic exploration for cooperative deep multi-agent reinforcement learning. Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran |
| 2023 | Conformal Risk Control for Ordinal Classification. Yunpeng Xu, Wenge Guo, Zhi Wei |
| 2023 | Content Sharing Design for Social Welfare in Networked Disclosure Game. Feiran Jia, Chenxi Qiu, Sarah Rajtmajer, Anna Cinzia Squicciarini |
| 2023 | Contrastive learning for supervised graph matching. Gathika Ratnayaka, Qing Wang, Yang Li |
| 2023 | Convergence rates for localized actor-critic in networked Markov potential games. Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman |
| 2023 | Copula for Instance-wise Feature Selection and Rank. Hanyu Peng, Guanhua Fang, Ping Li |
| 2023 | Copula-based deep survival models for dependent censoring. Ali Hossein Gharari Foomani, Michael Cooper, Russell Greiner, Rahul G. Krishnan |
| 2023 | Correcting for selection bias and missing response in regression using privileged information. Philip A. Boeken, Noud de Kroon, Mathijs de Jong, Joris M. Mooij, Onno Zoeter |
| 2023 | Counting Background Knowledge Consistent Markov Equivalent Directed Acyclic Graphs. Vidya Sagar Sharma |
| 2023 | CrysMMNet: Multimodal Representation for Crystal Property Prediction. Kishalay Das, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly |
| 2023 | Deep Gaussian mixture ensembles. Yousef El-Laham, Niccolò Dalmasso, Elizabeth Fons, Svitlana Vyetrenko |
| 2023 | DeepGD3: Unknown-Aware Deep Generative/Discriminative Hybrid Defect Detector for PCB Soldering Inspection. Ching-Wen Ma, Yanwei Liu |
| 2023 | Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics. Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam |
| 2023 | Differentiable user models. Alex Hämäläinen, Mustafa Mert Çelikok, Samuel Kaski |
| 2023 | Differential Privacy in Cooperative Multiagent Planning. Bo Chen, Calvin Hawkins, Mustafa O. Karabag, Cyrus Neary, Matthew T. Hale, Ufuk Topcu |
| 2023 | Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited. Jinyan Su, Changhong Zhao, Di Wang |
| 2023 | Differentially private synthetic data using KD-trees. Eleonora Kreacic, Navid Nouri, Vamsi K. Potluru, Tucker Balch, Manuela Veloso |
| 2023 | Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting. Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen |
| 2023 | Diversity-enhanced probabilistic ensemble for uncertainty estimation. Hanjing Wang, Qiang Ji |
| 2023 | Do we become wiser with time? On causal equivalence with tiered background knowledge. Christine W. Bang, Vanessa Didelez |
| 2023 | Does Momentum Help in Stochastic Optimization? A Sample Complexity Analysis. Swetha Ganesh, Rohan Deb, Gugan Thoppe, Amarjit Budhiraja |
| 2023 | E(2)-Equivariant Vision Transformer. Renjun Xu, Kaifan Yang, Ke Liu, Fengxiang He |
| 2023 | Efficient Failure Pattern Identification of Predictive Algorithms. Bao Nguyen, Viet Anh Nguyen |
| 2023 | Efficient Learning of Minimax Risk Classifiers in High Dimensions. Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez |
| 2023 | Efficient Privacy-Preserving Stochastic Nonconvex Optimization. Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu |
| 2023 | Efficiently learning the graph for semi-supervised learning. Dravyansh Sharma, Maxwell Jones |
| 2023 | Energy-based Predictive Representations for Partially Observed Reinforcement Learning. Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai |
| 2023 | Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration Estimator. Yuwen Cheng, Lili Wu, Shu Yang |
| 2023 | Establishing Markov equivalence in cyclic directed graphs. Tom Claassen, Joris M. Mooij |
| 2023 | Exact Count of Boundary Pieces of ReLU Classifiers: Towards the Proper Complexity Measure for Classification. Pawel Piwek, Adam Klukowski, Tianyang Hu |
| 2023 | Expectation consistency for calibration of neural networks. Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová |
| 2023 | Exploiting Inferential Structure in Neural Processes. Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric T. Nalisnick |
| 2023 | Exploration for Free: How Does Reward Heterogeneity Improve Regret in Cooperative Multi-agent Bandits? Xuchuang Wang, Lin Yang, Yu-Zhen Janice Chen, Xutong Liu, Mohammad Hajiesmaili, Don Towsley, John C. S. Lui |
| 2023 | FLASH: Automating federated learning using CASH. Md. Ibrahim Ibne Alam, Koushik Kar, Theodoros Salonidis, Horst Samulowitz |
| 2023 | Fairness-aware class imbalanced learning on multiple subgroups. Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Qi Long, Li Shen |
| 2023 | Fast Heterogeneous Federated Learning with Hybrid Client Selection. Duanxiao Song, Guangyuan Shen, Dehong Gao, Libin Yang, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou |
| 2023 | Fast Teammate Adaptation in the Presence of Sudden Policy Change. Ziqian Zhang, Lei Yuan, Lihe Li, Ke Xue, Chengxing Jia, Cong Guan, Chao Qian, Yang Yu |
| 2023 | Fast and scalable score-based kernel calibration tests. Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton |
| 2023 | Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning. Belhal Karimi, Ping Li, Xiaoyun Li |
| 2023 | Federated learning of models pre-trained on different features with consensus graphs. Tengfei Ma, Trong Nghia Hoang, Jie Chen |
| 2023 | Finding Invariant Predictors Efficiently via Causal Structure. Kenneth Lee, Md. Musfiqur Rahman, Murat Kocaoglu |
| 2023 | Finite-sample guarantees for Nash Q-learning with linear function approximation. Pedro Cisneros-Velarde, Sanmi Koyejo |
| 2023 | Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances. Anusha Lalitha, Kousha Kalantari, Yifei Ma, Anoop Deoras, Branislav Kveton |
| 2023 | Functional causal Bayesian optimization. Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa |
| 2023 | Gaussian Process Surrogate Models for Neural Networks. Michael Y. Li, Erin Grant, Thomas L. Griffiths |
| 2023 | Generating Synthetic Datasets by Interpolating along Generalized Geodesics. Jiaojiao Fan, David Alvarez-Melis |
| 2023 | Graph Self-supervised Learning via Proximity Distribution Minimization. Tianyi Zhang, Zhenwei Dai, Zhaozhuo Xu, Anshumali Shrivastava |
| 2023 | Graph classification Gaussian processes via spectral features. Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong |
| 2023 | Greed is good: correspondence recovery for unlabeled linear regression. Hang Zhang, Ping Li |
| 2023 | Guided Deep Kernel Learning. Idan Achituve, Gal Chechik, Ethan Fetaya |
| 2023 | Hallucinated adversarial control for conservative offline policy evaluation. Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause |
| 2023 | Heavy-tailed linear bandit with Huber regression. Minhyun Kang, Gi-Soo Kim |
| 2023 | Heteroskedastic Geospatial Tracking with Distributed Camera Networks. Colin Samplawski, Shiwei Fang, Ziqi Wang, Deepak Ganesan, Mani B. Srivastava, Benjamin M. Marlin |
| 2023 | How to use dropout correctly on residual networks with batch normalization. Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang, Donggeon Lee, Sang Woo Kim |
| 2023 | Human Control: Definitions and Algorithms. Ryan Carey, Tom Everitt |
| 2023 | Human-in-the-Loop Mixup. Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley C. Love, Adrian Weller |
| 2023 | Implicit Training of Inference Network Models for Structured Prediction. Shiv Shankar |
| 2023 | Improvable Gap Balancing for Multi-Task Learning. Yanqi Dai, Nanyi Fei, Zhiwu Lu |
| 2023 | In- or out-of-distribution detection via dual divergence estimation. Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka |
| 2023 | Incentivising Diffusion while Preserving Differential Privacy. Fengjuan Jia, Mengxiao Zhang, Jiamou Liu, Bakh Khoussainov |
| 2023 | Incentivizing honest performative predictions with proper scoring rules. Caspar Oesterheld, Johannes Treutlein, Emery Cooper, Rubi Hudson |
| 2023 | Increasing effect sizes of pairwise conditional independence tests between random vectors. Tom Hochsprung, Jonas Wahl, Andreas Gerhardus, Urmi Ninad, Jakob Runge |
| 2023 | Inference and sampling of point processes from diffusion excursions. Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh |
| 2023 | Inference for mark-censored temporal point processes. Alex Boyd, Yuxin Chang, Stephan Mandt, Padhraic Smyth |
| 2023 | Inference for probabilistic dependency graphs. Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa |
| 2023 | Inference of a rumor's source in the independent cascade model. Petra Berenbrink, Max Hahn-Klimroth, Dominik Kaaser, Lena Krieg, Malin Rau |
| 2023 | Information theoretic clustering via divergence maximization among clusters. Sahil Garg, Mina Dalirrooyfard, Anderson Schneider, Yeshaya Adler, Yuriy Nevmyvaka, Yu Chen, Fengpei Li, Guillermo A. Cecchi |
| 2023 | Interpretable differencing of machine learning models. Swagatam Haldar, Diptikalyan Saha, Dennis Wei, Rahul Nair, Elizabeth M. Daly |
| 2023 | Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals. Michael Jahn, Matthias Scheutz |
| 2023 | Is the volume of a credal set a good measure for epistemic uncertainty? Yusuf Sale, Michele Caprio, Eyke Hüllermeier |
| 2023 | Jana: Jointly amortized neural approximation of complex Bayesian models. Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner |
| 2023 | Keep-Alive Caching for the Hawkes process. Sushirdeep Narayana, Ian A. Kash |
| 2023 | Knowledge Intensive Learning of Cutset Networks. Saurabh Mathur, Vibhav Gogate, Sriraam Natarajan |
| 2023 | KrADagrad: Kronecker approximation-domination gradient preconditioned stochastic optimization. Jonathan Mei, Alexander Moreno, Luke Walters |
| 2023 | Learning Choice Functions with Gaussian Processes. Alessio Benavoli, Dario Azzimonti, Dario Piga |
| 2023 | Learning Nonlinear Causal Effect via Kernel Anchor Regression. Wenqi Shi, Wenkai Xu |
| 2023 | Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning. Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger |
| 2023 | Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud |
| 2023 | Learning good interventions in causal graphs via covering. Ayush Sawarni, Rahul Madhavan, Gaurav Sinha, Siddharth Barman |
| 2023 | Learning in online MDPs: is there a price for handling the communicating case? Gautam Chandrasekaran, Ambuj Tewari |
| 2023 | Learning robust representation for reinforcement learning with distractions by reward sequence prediction. Qi Zhou, Jie Wang, Qiyuan Liu, Yufei Kuang, Wengang Zhou, Houqiang Li |
| 2023 | Learning to reason about contextual knowledge for planning under uncertainty. Cheng Cui, Saeid Amiri, Yan Ding, Xingyue Zhan, Shiqi Zhang |
| 2023 | Lifelong bandit optimization: no prior and no regret. Felix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause |
| 2023 | Local Message Passing on Frustrated Systems. Luca Schmid, Joshua Brenk, Laurent Schmalen |
| 2023 | Locally Regularized Sparse Graph by Fast Proximal Gradient Descent. Dongfang Sun, Yingzhen Yang |
| 2023 | Logit-based ensemble distribution distillation for robust autoregressive sequence uncertainties. Yassir Fathullah, Guoxuan Xia, Mark J. F. Gales |
| 2023 | Loosely consistent emphatic temporal-difference learning. Jiamin He, Fengdi Che, Yi Wan, A. Rupam Mahmood |
| 2023 | Low-rank matrix recovery with unknown correspondence. Zhiwei Tang, Tsung-Hui Chang, Xiaojing Ye, Hongyuan Zha |
| 2023 | MDPose: real-time multi-person pose estimation via mixture density model. Seunghyeon Seo, Jaeyoung Yoo, Jihye Hwang, Nojun Kwak |
| 2023 | MFA: Multi-layer Feature-aware Attack for Object Detection. Wen Chen, Yushan Zhang, Zhiheng Li, Yuehuan Wang |
| 2023 | MMEL: A Joint Learning Framework for Multi-Mention Entity Linking. Chengmei Yang, Bowei He, Yimeng Wu, Chao Xing, Lianghua He, Chen Ma |
| 2023 | Massively parallel reweighted wake-sleep. Thomas Heap, Gavin Leech, Laurence Aitchison |
| 2023 | Maximizing submodular functions under submodular constraints. Madhavan R. Padmanabhan, Yanhui Zhu, Samik Basu, Aduri Pavan |
| 2023 | Memory Mechanism for Unsupervised Anomaly Detection. Jiahao Li, Yiqiang Chen, Yunbing Xing |
| 2023 | Meta-learning Control Variates: Variance Reduction with Limited Data. Zhuo Sun, Chris J. Oates, François-Xavier Briol |
| 2023 | Mitigating Transformer Overconfidence via Lipschitz Regularization. Wenqian Ye, Yunsheng Ma, Xu Cao, Kun Tang |
| 2023 | Mixture of Normalizing Flows for European Option Pricing. Yongxin Yang, Timothy M. Hospedales |
| 2023 | MixupE: Understanding and improving Mixup from directional derivative perspective. Yingtian Zou, Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi |
| 2023 | Mnemonist: Locating Model Parameters that Memorize Training Examples. Ali Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller |
| 2023 | Modified Retrace for Off-Policy Temporal Difference Learning. Xingguo Chen, Xingzhou Ma, Yang Li, Guang Yang, Shangdong Yang, Yang Gao |
| 2023 | Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu |
| 2023 | Monte-Carlo Search for an Equilibrium in Dec-POMDPs. Yang You, Vincent Thomas, Francis Colas, Olivier Buffet |
| 2023 | Multi-View Independent Component Analysis with Shared and Individual Sources. Teodora Pandeva, Patrick Forré |
| 2023 | Multi-modal differentiable unsupervised feature selection. Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe |
| 2023 | Multi-view graph contrastive learning for solving vehicle routing problems. Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang |
| 2023 | Neural probabilistic logic programming in discrete-continuous domains. Lennert De Smet, Pedro Zuidberg Dos Martires, Robin Manhaeve, Giuseppe Marra, Angelika Kimmig, Luc De Raedt |
| 2023 | Neural tangent kernel at initialization: linear width suffices. Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin |
| 2023 | No-Regret Linear Bandits beyond Realizability. Chong Liu, Ming Yin, Yu-Xiang Wang |
| 2023 | Noisy adversarial representation learning for effective and efficient image obfuscation. Jonghu Jeong, Minyong Cho, Philipp Benz, Tae-Hoon Kim |
| 2023 | Nonconvex stochastic scaled gradient descent and generalized eigenvector problems. Chris Junchi Li, Michael I. Jordan |
| 2023 | Nyström M-Hilbert-Schmidt independence criterion. Florian Kalinke, Zoltán Szabó |
| 2023 | On Identifiability of Conditional Causal Effects. Yaroslav Kivva, Jalal Etesami, Negar Kiyavash |
| 2023 | On Minimizing the Impact of Dataset Shifts on Actionable Explanations. Anna P. Meyer, Dan Ley, Suraj Srinivas, Himabindu Lakkaraju |
| 2023 | On Testability and Goodness of Fit Tests in Missing Data Models. Razieh Nabi, Rohit Bhattacharya |
| 2023 | On inference and learning with probabilistic generating circuits. Juha Harviainen, Vaidyanathan Peruvemba Ramaswamy, Mikko Koivisto |
| 2023 | On the Convergence of Continual Learning with Adaptive Methods. Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee |
| 2023 | On the Relation between Policy Improvement and Off-Policy Minimum-Variance Policy Evaluation. Alberto Maria Metelli, Samuele Meta, Marcello Restelli |
| 2023 | On the Role of Generalization in Transferability of Adversarial Examples. Yilin Wang, Farzan Farnia |
| 2023 | On the informativeness of supervision signals. Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh, Joshua C. Peterson, Pulkit Singh, Umang Bhatt, Nori Jacoby, Adrian Weller, Thomas L. Griffiths |
| 2023 | On the limitations of Markovian rewards to express multi-objective, risk-sensitive, and modal tasks. Joar Skalse, Alessandro Abate |
| 2023 | On the role of model uncertainties in Bayesian optimisation. Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen |
| 2023 | Online Heavy-tailed Change-point detection. Abishek Sankararaman, Balakrishnan Narayanaswamy |
| 2023 | Online estimation of similarity matrices with incomplete data. Fangchen Yu, Yicheng Zeng, Jianfeng Mao, Wenye Li |
| 2023 | Optimal Budget Allocation for Crowdsourcing Labels for Graphs. Adithya Kulkarni, Mohna Chakraborty, Sihong Xie, Qi Li |
| 2023 | Optimistic Thompson Sampling-based algorithms for episodic reinforcement learning. Bingshan Hu, Tianyue H. Zhang, Nidhi Hegde, Mark Schmidt |
| 2023 | Overcoming Language Priors for Visual Question Answering via Loss Rebalancing Label and Global Context. Runlin Cao, Zhixin Li |
| 2023 | Pandering in a (flexible) representative democracy. Xiaolin Sun, Jacob Masur, Ben Abramowitz, Nicholas Mattei, Zizhan Zheng |
| 2023 | Parity calibration. Youngseog Chung, Aaron Rumack, Chirag Gupta |
| 2023 | Partial identification of dose responses with hidden confounders. Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg |
| 2023 | Personalized federated domain adaptation for item-to-item recommendation. Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang |
| 2023 | Pessimistic Model Selection for Offline Deep Reinforcement Learning. Chao-Han Huck Yang, Zhengling Qi, Yifan Cui, Pin-Yu Chen |
| 2023 | Phase-shifted adversarial training. Yeachan Kim, Seongyeon Kim, Ihyeok Seo, Bonggun Shin |
| 2023 | Piecewise Deterministic Markov Processes for Bayesian Neural Networks. Ethan Goan, Dimitri Perrin, Kerrie L. Mengersen, Clinton Fookes |
| 2023 | Posterior sampling-based online learning for the stochastic shortest path model. Mehdi Jafarnia-Jahromi, Liyu Chen, Rahul Jain, Haipeng Luo |
| 2023 | Practical privacy-preserving Gaussian process regression via secret sharing. Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Shuang Qin, Hui Wang, Yue Yu, Zenglin Xu |
| 2023 | Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter. Yuqing Zhu, Xuandong Zhao, Chuan Guo, Yu-Xiang Wang |
| 2023 | Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference. Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan |
| 2023 | Probabilistic Multi-Dimensional Classification. Vu-Linh Nguyen, Yang Yang, Cassio de Campos |
| 2023 | Probabilistic circuits that know what they don't know. Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting |
| 2023 | Probabilistically robust conformal prediction. Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja, Yan Yan, Jana Doppa, Brian Jones |
| 2023 | Provably Efficient Adversarial Imitation Learning with Unknown Transitions. Tian Xu, Ziniu Li, Yang Yu, Zhi-Quan Luo |
| 2023 | Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu |
| 2023 | Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, Eyke Hüllermeier |
| 2023 | Quantifying lottery tickets under label noise: accuracy, calibration, and complexity. Viplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti |
| 2023 | Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates. Sahra Ghalebikesabi, Chris C. Holmes, Edwin Fong, Brieuc Lehmann |
| 2023 | RDM-DC: Poisoning Resilient Dataset Condensation with Robust Distribution Matching. Tianhang Zheng, Baochun Li |
| 2023 | Random Reshuffling with Variance Reduction: New Analysis and Better Rates. Grigory Malinovsky, Alibek Sailanbayev, Peter Richtárik |
| 2023 | Regularized online DR-submodular optimization. Pengyu Zuo, Yao Wang, Shaojie Tang |
| 2023 | Residual-based error bound for physics-informed neural networks. Shuheng Liu, Xiyue Huang, Pavlos Protopapas |
| 2023 | Revisiting Bayesian network learning with small vertex cover. Juha Harviainen, Mikko Koivisto |
| 2023 | Reward-machine-guided, self-paced reinforcement learning. Cevahir Köprülü, Ufuk Topcu |
| 2023 | Risk-aware curriculum generation for heavy-tailed task distributions. Cevahir Köprülü, Thiago D. Simão, Nils Jansen, Ufuk Topcu |
| 2023 | Risk-limiting financial audits via weighted sampling without replacement. Shubhanshu Shekhar, Ziyu Xu, Zachary C. Lipton, Pierre J. Liang, Aaditya Ramdas |
| 2023 | Robust Gaussian process regression with the trimmed marginal likelihood. Daniel Andrade, Akiko Takeda |
| 2023 | Robust Quickest Change Detection for Unnormalized Models. Suya Wu, Enmao Diao, Jie Ding, Taposh Banerjee, Vahid Tarokh |
| 2023 | Robust distillation for worst-class performance: on the interplay between teacher and student objectives. Serena Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon |
| 2023 | Robust statistical comparison of random variables with locally varying scale of measurement. Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin |
| 2023 | SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models. Vithursan Thangarasa, Abhay Gupta, William Marshall, Tianda Li, Kevin Leong, Dennis DeCoste, Sean Lie, Shreyas Saxena |
| 2023 | Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data. Baptiste Bauvin, Cécile Capponi, Florence Clerc, Pascal Germain, Sokol Koço, Jacques Corbeil |
| 2023 | Scalable and robust tensor ring decomposition for large-scale data. Yicong He, George K. Atia |
| 2023 | Scalable nonparametric Bayesian learning for dynamic velocity fields. Sunrit Chakraborty, Aritra Guha, Rayleigh Lei, XuanLong Nguyen |
| 2023 | Scaling integer arithmetic in probabilistic programs. William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd D. Millstein, Guy Van den Broeck |
| 2023 | Semi-supervised learning of partial differential operators and dynamical flows. Michael Rotman, Amit Dekel, Ran Ilan Ber, Lior Wolf, Yaron Oz |
| 2023 | Simple Transferability Estimation for Regression Tasks. Cuong N. Nguyen, Phong Tran, Lam Si Tung Ho, Vu C. Dinh, Anh T. Tran, Tal Hassner, Cuong V. Nguyen |
| 2023 | Size-constrained k-submodular maximization in near-linear time. Guanyu Nie, Yanhui Zhu, Yididiya Y. Nadew, Samik Basu, A. Pavan, Christopher John Quinn |
| 2023 | Solving multi-model MDPs by coordinate ascent and dynamic programming. Xihong Su, Marek Petrik |
| 2023 | Split, count, and share: a differentially private set intersection cardinality estimation protocol. Michael Purcell, Yang Li, Kee Siong Ng |
| 2023 | Stochastic Generative Flow Networks. Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio |
| 2023 | Stochastic Graphical Bandits with Heavy-Tailed Rewards. Yutian Gou, Jinfeng Yi, Lijun Zhang |
| 2023 | Structure-aware robustness certificates for graph classification. Pierre Osselin, Henry Kenlay, Xiaowen Dong |
| 2023 | Studying the Effect of GNN Spatial Convolutions On The Embedding Space's Geometry. Claire Donnat, Sowon Jeong |
| 2023 | SubMix: Learning to Mix Graph Sampling Heuristics. Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger, Bryan Perozzi, MohammadHossein Bateni |
| 2023 | Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms. Anna Guo, Jiwei Zhao, Razieh Nabi |
| 2023 | SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs. Vishal Sharma, Daman Arora, Mausam, Parag Singla |
| 2023 | TCE: A Test-Based Approach to Measuring Calibration Error. Takuo Matsubara, Niek Tax, Richard Mudd, Ido Guy |
| 2023 | Testing conventional wisdom (of the crowd). Noah Burrell, Grant Schoenebeck |
| 2023 | The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference. Charles C. Margossian, Lawrence K. Saul |
| 2023 | The past does matter: correlation of subsequent states in trajectory predictions of Gaussian Process models. Steffen Ridderbusch, Sina Ober-Blöbaum, Paul Goulart |
| 2023 | Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Prediction. Chengmin Gao, Bin Li |
| 2023 | Towards Physically Reliable Molecular Representation Learning. Seunghoon Yi, Youngwoo Cho, Jinhwan Sul, Seung Woo Ko, Soo Kyung Kim, Jaegul Choo, Hongkee Yoon, Joonseok Lee |
| 2023 | Towards better certified segmentation via diffusion models. Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon, Marie-Pierre Revel, Siddharth Garg, Farshad Khorrami, Maria Vakalopoulou |
| 2023 | Transfer learning for individual treatment effect estimation. Ahmed Aloui, Juncheng Dong, Cat P. Le, Vahid Tarokh |
| 2023 | Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration. Shuang Ao, Stefan Rueger, Advaith Siddharthan |
| 2023 | Two-phase Attacks in Security Games. Andrzej Nagórko, Pawel Ciosmak, Tomasz P. Michalak |
| 2023 | Two-stage Kernel Bayesian Optimization in High Dimensions. Jian Tan, Niv Nayman |
| 2023 | Two-stage holistic and contrastive explanation of image classification. Weiyan Xie, Xiao-Hui Li, Zhi Lin, Leonard K. M. Poon, Caleb Chen Cao, Nevin L. Zhang |
| 2023 | USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution. Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, Biplab Banerjee |
| 2023 | Uncertainty in Artificial Intelligence, UAI 2023, July 31 - 4 August 2023, Pittsburgh, PA, USA. Robin J. Evans, Ilya Shpitser |
| 2023 | Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. Yue Wu, Jiafan He, Quanquan Gu |
| 2023 | Universal Graph Contrastive Learning with a Novel Laplacian Perturbation. Taewook Ko, Yoonhyuk Choi, Chong-Kwon Kim |
| 2023 | Vacant holes for unsupervised detection of the outliers in compact latent representation. Misha Glazunov, Apostolis Zarras |
| 2023 | Validation of composite systems by discrepancy propagation. David Reeb, Kanil Patel, Karim Said Barsim, Martin Schiegg, Sebastian Gerwinn |
| 2023 | Variable importance matching for causal inference. Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page |
| 2023 | ViBid: Linear Vision Transformer with Bidirectional Normalization. Jeonggeun Song, Heung-Chang Lee |
| 2023 | When are post-hoc conceptual explanations identifiable? Tobias Leemann, Michael Kirchhof, Yao Rong, Enkelejda Kasneci, Gjergji Kasneci |
| 2023 | Why Out-of-Distribution detection experiments are not reliable - subtle experimental details muddle the OOD detector rankings. Kamil Szyc, Tomasz Walkowiak, Henryk Maciejewski |