| 2025 | Function-Space MCMC for Bayesian Wide Neural Networks. | Lucia Pezzetti, Stefano Favaro, Stefano Peluchetti |
| 2025 | Learning signals defined on graphs with optimal transport and Gaussian process regression. | Raphal Carpintero Perez, Sbastien Da Veiga, Josselin Garnier, Brian Staber |
| 2025 | Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration. | Alexandre Perez-Lebel, Gal Varoquaux, Sanmi Koyejo, Matthieu Doutreligne, Marine Le Morvan |
| 2025 | BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments. | Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Ricardo Silva, David S. Watson |
| 2025 | Nonparametric Distributional Regression via Quantile Regression. | Cheng Peng, Stan Uryasev |
| 2025 | Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness. | Nikola Pavlovic, Sudeep Salgia, Qing Zhao |
| 2025 | Differentially Private Kernelized Contextual Bandits. | Nikola Pavlovic, Sudeep Salgia, Qing Zhao |
| 2025 | Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics. | Daniel Paulin, Peter A. Whalley, Neil K. Chada, Benedict J. Leimkuhler |
| 2025 | FedBaF: Federated Learning Aggregation Biased by a Foundation Model. | Jong-Ik Park, Srinivasa Pranav, Jos M. F. Moura, Carlee Joe-Wong |
| 2025 | Infinite-Horizon Reinforcement Learning with Multinomial Logit Function Approximation. | Jaehyun Park, Junyeop Kwon, Dabeen Lee |
| 2025 | Semiparametric conformal prediction. | Ji Won Park, Kyunghyun Cho |
| 2025 | Approximate Equivariance in Reinforcement Learning. | Jung Yeon Park, Sujay Bhatt, Sihan Zeng, Lawson L. S. Wong, Alec Koppel, Sumitra Ganesh, Robin Walters |
| 2025 | Copula Based Trainable Calibration Error Estimator of Multi-Label Classification with Label Interdependencies. | Arkapal Panda, Utpal Garain |
| 2025 | Local Stochastic Sensitivity Analysis For Dynamical Systems. | Nishant Panda, Jehanzeb H. Chaudhry, Natalie Klein, James Carzon, Troy D. Butler |
| 2025 | A Causal Framework for Evaluating Deferring Systems. | Filippo Palomba, Andrea Pugnana, Jos M. lvarez, Salvatore Ruggieri |
| 2025 | Stochastic Rounding for LLM Training: Theory and Practice. | Kaan Ozkara, Tao Yu, Youngsuk Park |
| 2025 | ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning. | Kaan Ozkara, Bruce Huang, Ruida Zhou, Suhas N. Diggavi |
| 2025 | The Uniformly Rotated Mondrian Kernel. | Calvin Osborne, Eliza O'Reilly |
| 2025 | Cross Validation for Correlated Data in Classification Models. | Yuval Oren, Saharon Rosset |
| 2025 | All models are wrong, some are useful: Model Selection with Limited Labels. | Patrik Okanovic, Andreas Kirsch, Jannes Kasper, Torsten Hoefler, Andreas Krause, Nezihe Merve Grel |
| 2025 | Optimal estimation of linear non-Gaussian structure equation models. | Sunmin Oh, Seungsu Han, Gunwoong Park |
| 2025 | Weighted Sum of Gaussian Process Latent Variable Models. | James Odgers, Ruby Sedgwick, Chrysoula Kappatou, Ruth Misener, Sarah Filippi |
| 2025 | A Multi-Task Learning Approach to Linear Multivariate Forecasting. | Liran Nochumsohn, Hedi Zisling, Omri Azencot |
| 2025 | Efficient Estimation of a Gaussian Mean with Local Differential Privacy. | Kalinin Nikita, Lukas Steinberger |
| 2025 | Policy Teaching via Data Poisoning in Learning from Human Preferences. | Andi Nika, Jonathan Nther, Debmalya Mandal, Parameswaran Kamalaruban, Adish Singla, Goran Radanovic |