Benchmarking Model-Based Reinforcement Learning. - Tingwu Wang; Xuchan Bao; Ignasi Clavera; Jerrick Hoang; Yeming Wen; Eric D. Langlois 002; Shunshi Zhang 001; Guodong Zhang 006; Pieter Abbeel; Jimmy Ba (CoRR, 2019)

One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation. - Matthew Shunshi Zhang; Bradly C. Stadie (ICLR, 2020)

Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings. - Matthew Shunshi Zhang; Murat A. Erdogdu; Animesh Garg (AAAI, 2022)

Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence. - Murat A. Erdogdu; Rasa Hosseinzadeh; Shunshi Zhang 001 (AISTATS, 2022)

Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo. - Krishna Balasubramanian; Sinho Chewi; Murat A. Erdogdu; Adil Salim; Shunshi Zhang 001 (COLT, 2022)

Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev. - Sinho Chewi; Murat A. Erdogdu; Mufan (Bill) Li; Ruoqi Shen; Shunshi Zhang 001 (COLT, 2022)

Improved Discretization Analysis for Underdamped Langevin Monte Carlo. - Matthew Shunshi Zhang; Sinho Chewi; Mufan (Bill) Li; Krishna Balasubramanian; Murat A. Erdogdu (COLT, 2023)

Sampling from the Mean-Field Stationary Distribution. - Yunbum Kook; Matthew Shunshi Zhang; Sinho Chewi; Murat A. Erdogdu; Mufan (Bill) Li (COLT, 2024)

In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies. - Yunbum Kook; Santosh S. Vempala; Matthew Shunshi Zhang (NeurIPS, 2024)

Sampling from the Mean-Field Stationary Distribution. - Yunbum Kook; Matthew Shunshi Zhang; Sinho Chewi; Murat A. Erdogdu; Mufan Bill Li (CoRR, 2024)