| 2021 | Reinforcement Learning for Constrained Markov Decision Processes. | Ather Gattami, Qinbo Bai, Vaneet Aggarwal |
| 2021 | Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. | Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu |
| 2021 | Causal Inference with Selectively Deconfounded Data. | Kyra Gan, Andrew A. Li, Zachary Chase Lipton, Sridhar R. Tayur |
| 2021 | Selective Classification via One-Sided Prediction. | Aditya Gangrade, Anil Kag, Venkatesh Saligrama |
| 2021 | vqSGD: Vector Quantized Stochastic Gradient Descent. | Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar |
| 2021 | γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. | Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama |
| 2021 | Free-rider Attacks on Model Aggregation in Federated Learning. | Yann Fraboni, Richard Vidal, Marco Lorenzi |
| 2021 | Aggregating Incomplete and Noisy Rankings. | Dimitris Fotakis, Alkis Kalavasis, Konstantinos Stavropoulos |
| 2021 | Measure Transport with Kernel Stein Discrepancy. | Matthew Fisher, Tui Nolan, Matthew M. Graham, Dennis Prangle, Chris J. Oates |
| 2021 | A Contraction Approach to Model-based Reinforcement Learning. | Ting-Han Fan, Peter J. Ramadge |
| 2021 | A Variational Inference Approach to Learning Multivariate Wold Processes. | Jalal Etesami, William Trouleau, Negar Kiyavash, Matthias Grossglauser, Patrick Thiran |
| 2021 | Scalable Constrained Bayesian Optimization. | David Eriksson, Matthias Poloczek |
| 2021 | Fisher Auto-Encoders. | Khalil Elkhalil, Ali Hasan, Jie Ding, Sina Farsiu, Vahid Tarokh |
| 2021 | On the role of data in PAC-Bayes. | Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel M. Roy |
| 2021 | Improved Complexity Bounds in Wasserstein Barycenter Problem. | Darina Dvinskikh, Daniil Tiapkin |
| 2021 | Parametric Programming Approach for More Powerful and General Lasso Selective Inference. | Vo Nguyen Le Duy, Ichiro Takeuchi |
| 2021 | On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. | Alain Durmus, Pablo Jimnez, Eric Moulines, Salem Said |
| 2021 | A constrained risk inequality for general losses. | John C. Duchi, Feng Ruan |
| 2021 | Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects. | Qiming Du, Grard Biau, Franois Petit, Raphal Porcher |
| 2021 | No-Regret Algorithms for Private Gaussian Process Bandit Optimization. | Abhimanyu Dubey |
| 2021 | A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. | Omar Darwiche Domingues, Pierre Mnard, Matteo Pirotta, Emilie Kaufmann, Michal Valko |
| 2021 | A Bayesian nonparametric approach to count-min sketch under power-law data streams. | Emanuele Dolera, Stefano Favaro, Stefano Peluchetti |
| 2021 | A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. | Thang Doan, Mehdi Abbana Bennani, Bogdan Mazoure, Guillaume Rabusseau, Pierre Alquier |
| 2021 | Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms. | Tianyu Ding, Zhihui Zhu, Manolis C. Tsakiris, Ren Vidal, Daniel P. Robinson |
| 2021 | Provably Efficient Safe Exploration via Primal-Dual Policy Optimization. | Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo R. Jovanovic |