| 2023 | A Constant-Factor Approximation Algorithm for Reconciliation k-Median. | Joachim Spoerhase, Kamyar Khodamoradi, Benedikt Riegel, Bruno Ordozgoiti, Aristides Gionis |
| 2023 | Prediction-Oriented Bayesian Active Learning. | Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth |
| 2023 | Nonparametric Indirect Active Learning. | Shashank Singh |
| 2023 | Multi-armed Bandit Experimental Design: Online Decision-making and Adaptive Inference. | David Simchi-Levi, Chonghuan Wang |
| 2023 | CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision. | Aman Shrivastava, Ramprasaath R. Selvaraju, Nikhil Naik, Vicente Ordonez |
| 2023 | The Lauritzen-Chen Likelihood For Graphical Models. | Ilya Shpitser |
| 2023 | Loss-Curvature Matching for Dataset Selection and Condensation. | Seungjae Shin, HeeSun Bae, DongHyeok Shin, Weonyoung Joo, Il-Chul Moon |
| 2023 | Distributed Offline Policy Optimization Over Batch Data. | Han Shen, Songtao Lu, Xiaodong Cui, Tianyi Chen |
| 2023 | PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time. | Jie Shen |
| 2023 | On the Capacity Limits of Privileged ERM. | Michal Sharoni, Sivan Sabato |
| 2023 | Do Bayesian Neural Networks Need To Be Fully Stochastic? | Mrinank Sharma, Sebastian Farquhar, Eric T. Nalisnick, Tom Rainforth |
| 2023 | Beyond Performative Prediction: Open-environment Learning with Presence of Corruptions. | Jia-Wei Shan, Peng Zhao, Zhi-Hua Zhou |
| 2023 | Direct Inference of Effect of Treatment (DIET) for a Cookieless World. | Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Madalina Fiterau |
| 2023 | Precision/Recall on Imbalanced Test Data. | Hongwei Shang, Jean-Marc Langlois, Kostas Tsioutsiouliklis, Changsung Kang |
| 2023 | Model-X Sequential Testing for Conditional Independence via Testing by Betting. | Shalev Shaer, Gal Maman, Yaniv Romano |
| 2023 | NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning. | Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Htter |
| 2023 | Mixtures of All Trees. | Nikil Roashan Selvam, Honghua Zhang, Guy Van den Broeck |
| 2023 | Sparse Spectral Bayesian Permanental Process with Generalized Kernel. | Jeremy Sellier, Petros Dellaportas |
| 2023 | Improving Adaptive Conformal Prediction Using Self-Supervised Learning. | Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar |
| 2023 | Meta-Uncertainty in Bayesian Model Comparison. | Marvin Schmitt, Stefan T. Radev, Paul-Christian Brkner |
| 2023 | Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond. | Meyer Scetbon, Elvis Dohmatob |
| 2023 | Mode-constrained Model-based Reinforcement Learning via Gaussian Processes. | Aidan Scannell, Carl Henrik Ek, Arthur Richards |
| 2023 | Risk-aware linear bandits with convex loss. | Patrick Saux, Odalric Maillard |
| 2023 | Implications of sparsity and high triangle density for graph representation learning. | Hannah Sansford, Alexander Modell, Nick Whiteley, Patrick Rubin-Delanchy |
| 2023 | Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck. | Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash, Taps Maiti, Gustavo de los Campos, Ian Fischer |