| 2024 | Bayesian Online Learning for Consensus Prediction. | Samuel Showalter, Alex J. Boyd, Padhraic Smyth, Mark Steyvers |
| 2024 | Identification and Estimation of "Causes of Effects" using Covariate-Mediator Information. | Ryusei Shingaki, Manabu Kuroki |
| 2024 | Learning Cartesian Product Graphs with Laplacian Constraints. | Changhao Shi, Gal Mishne |
| 2024 | Adaptive and non-adaptive minimax rates for weighted Laplacian-Eigenmap based nonparametric regression. | Zhaoyang Shi, Krishna Balasubramanian, Wolfgang Polonik |
| 2024 | Efficient Variational Sequential Information Control. | Jianwei Shen, Jason Pacheco |
| 2024 | Continual Domain Adversarial Adaptation via Double-Head Discriminators. | Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao |
| 2024 | Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization. | Wei Shen, Minhui Huang, Jiawei Zhang, Cong Shen |
| 2024 | Strategic Usage in a Multi-Learner Setting. | Eliot Shekhtman, Sarah Dean |
| 2024 | Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting. | Louis Sharrock, Daniel Dodd, Christopher Nemeth |
| 2024 | Nonparametric Automatic Differentiation Variational Inference with Spline Approximation. | Yuda Shao, Shan Yu, Tianshu Feng |
| 2024 | A/B testing under Interference with Partial Network Information. | Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau |
| 2024 | Weight-Sharing Regularization. | Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien |
| 2024 | Estimation of partially known Gaussian graphical models with score-based structural priors. | Martin Sevilla, Antonio G. Marques, Santiago Segarra |
| 2024 | Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks. | Marcus A. K. September, Francesco Sanna Passino, Leonie Tabea Goldmann, Anton Hinel |
| 2024 | Structured Transforms Across Spaces with Cost-Regularized Optimal Transport. | Othmane Sebbouh, Marco Cuturi, Gabriel Peyr |
| 2024 | Adaptive Quasi-Newton and Anderson Acceleration Framework with Explicit Global (Accelerated) Convergence Rates. | Damien Scieur |
| 2024 | Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles. | Kevin Scaman, Mathieu Even, Batiste Le Bars, Laurent Massouli |
| 2024 | Error bounds for any regression model using Gaussian processes with gradient information. | Rafael Savvides, Hoang Phuc Hau Luu, Kai Puolamki |
| 2024 | Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training. | Tom Sander, Maxime Sylvestre, Alain Durmus |
| 2024 | Causally Inspired Regularization Enables Domain General Representations. | Olawale Salaudeen, Sanmi Koyejo |
| 2024 | Testing exchangeability by pairwise betting. | Aytijhya Saha, Aaditya Ramdas |
| 2024 | Faster Convergence with MultiWay Preferences. | Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren |
| 2024 | Scalable Higher-Order Tensor Product Spline Models. | David Rgamer |
| 2024 | Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. | Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe |
| 2024 | Electronic Medical Records Assisted Digital Clinical Trial Design. | Xinrui Ruan, Jingshen Wang, Yingfei Wang, Waverly Wei |