FedSplit: an algorithmic framework for fast federated optimization. - Reese Pathak; Martin J. Wainwright (NeurIPS, 2020)

Cluster-and-Conquer: A Framework For Time-Series Forecasting. - Reese Pathak; Rajat Sen; Nikhil Rao 001; N. Benjamin Erichson; Michael I. Jordan; Inderjit S. Dhillon (CoRR, 2021)

Weighted Matrix Completion From Non-Random, Non-Uniform Sampling Patterns. - Simon Foucart; Deanna Needell; Reese Pathak; Yaniv Plan; Mary Wootters (IEEE Trans. Inf. Theory, 2021)

A new similarity measure for covariate shift with applications to nonparametric regression. - Reese Pathak; Cong Ma 001; Martin J. Wainwright (ICML, 2022)

Optimally tackling covariate shift in RKHS-based nonparametric regression. - Cong Ma 001; Reese Pathak; Martin J. Wainwright (CoRR, 2022)

Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints. - Reese Pathak; Martin J. Wainwright; Lin Xiao (CoRR, 2023)

Transformers can optimally learn regression mixture models. - Reese Pathak; Rajat Sen; Weihao Kong; Abhimanyu Das (ICLR, 2024)

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction. - Drew T. Nguyen; Reese Pathak; Anastasios N. Angelopoulos; Stephen Bates; Michael I. Jordan (CoRR, 2024)

Revisiting mean estimation over ℓp balls: Is the MLE optimal? - Liviu Aolaritei; Michael I. Jordan; Reese Pathak; Annie Ulichney (CoRR, 2025)