| 2023 | Mixed Linear Regression via Approximate Message Passing. | Nelvin Tan, Ramji Venkataramanan |
| 2023 | A Blessing of Dimensionality in Membership Inference through Regularization. | Jasper Tan, Daniel LeJeune, Blake Mason, Hamid Javadi, Richard G. Baraniuk |
| 2023 | Minimax Nonparametric Two-Sample Test under Adversarial Losses. | Rong Tang, Yun Yang |
| 2023 | Wasserstein Distributional Learning via Majorization-Minimization. | Chengliang Tang, Nathan Lenssen, Ying Wei, Tian Zheng |
| 2023 | Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models. | Naoya Takeishi, Alexandros Kalousis |
| 2023 | The Power of Recursion in Graph Neural Networks for Counting Substructures. | Behrooz Tahmasebi, Derek Lim, Stefanie Jegelka |
| 2023 | On Generalization of Decentralized Learning with Separable Data. | Hossein Taheri, Christos Thrampoulidis |
| 2023 | Retrospective Uncertainties for Deep Models using Vine Copulas. | Natasa Tagasovska, Firat Ozdemir, Axel Brando |
| 2023 | Posterior Tracking Algorithm for Classification Bandits. | Koji Tabata, Junpei Komiyama, Atsuyoshi Nakamura, Tamiki Komatsuzaki |
| 2023 | Smoothly Giving up: Robustness for Simple Models. | Tyler Sypherd, Nathaniel Stromberg, Richard Nock, Visar Berisha, Lalitha Sankar |
| 2023 | Uni6Dv2: Noise Elimination for 6D Pose Estimation. | Mingshan Sun, Ye Zheng, Tianpeng Bao, Jianqiu Chen, Guoqiang Jin, Liwei Wu, Rui Zhao, Xiaoke Jiang |
| 2023 | NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge. | Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart |
| 2023 | A Unified Perspective on Regularization and Perturbation in Differentiable Subset Selection. | Xiangqian Sun, Cheuk Hang Leung, Yijun Li, Qi Wu |
| 2023 | Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition. | Lukang Sun, Avetik G. Karagulyan, Peter Richtrik |
| 2023 | Discrete Langevin Samplers via Wasserstein Gradient Flow. | Haoran Sun, Hanjun Dai, Bo Dai, Haomin Zhou, Dale Schuurmans |
| 2023 | DIET: Conditional independence testing with marginal dependence measures of residual information. | Mukund Sudarshan, Aahlad Manas Puli, Wesley Tansey, Rajesh Ranganath |
| 2023 | PAC-Bayesian Learning of Optimization Algorithms. | Michael Sucker, Peter Ochs |
| 2023 | Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees. | Hsin-En Su, Yen-Ju Chen, Ping-Chun Hsieh, Xi Liu |
| 2023 | Bounding Evidence and Estimating Log-Likelihood in VAE. | Lukasz Struski, Marcin Mazur, Pawel Batorski, Przemyslaw Spurek, Jacek Tabor |
| 2023 | Sampling From a Schrdinger Bridge. | Austin J. Stromme |
| 2023 | The Ordered Matrix Dirichlet for State-Space Models. | Niklas Stoehr, Benjamin J. Radford, Ryan Cotterell, Aaron Schein |
| 2023 | Data Augmentation for Imbalanced Regression. | Samuel Stocksieker, Denys Pommeret, Arthur Charpentier |
| 2023 | Faithful Heteroscedastic Regression with Neural Networks. | Andrew Stirn, Harm Wessels, Megan Schertzer, Laura Pereira, Neville E. Sanjana, David A. Knowles |
| 2023 | Regression as Classification: Influence of Task Formulation on Neural Network Features. | Lawrence Stewart, Francis R. Bach, Quentin Berthet, Jean-Philippe Vert |
| 2023 | Bayesian Optimization with Conformal Prediction Sets. | Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson |