Distributed Bandit Learning: How Much Communication is Needed to Achieve (Near) Optimal Regret. - Yuanhao Wang 001; Jiachen Hu; Xiaoyu Chen 008; Liwei Wang 001 (CoRR, 2019)

Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP. - Yuanhao Wang 001; Kefan Dong; Xiaoyu Chen 008; Liwei Wang 001 (ICLR, 2020)

Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication. - Yuanhao Wang 001; Jiachen Hu; Xiaoyu Chen 008; Liwei Wang 001 (ICLR, 2020)

On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach. - Yuanhao Wang 001; Guodong Zhang 006; Jimmy Ba (ICLR, 2020)

Improved Algorithms for Convex-Concave Minimax Optimization. - Yuanhao Wang 001; Jian Li (NeurIPS, 2020)

Refined Analysis of FPL for Adversarial Markov Decision Processes. - Yuanhao Wang 001; Kefan Dong (CoRR, 2020)

Provably Efficient Online Agnostic Learning in Markov Games. - Yi Tian; Yuanhao Wang 001; Tiancheng Yu; Suvrit Sra (CoRR, 2020)

On the Suboptimality of Negative Momentum for Minimax Optimization. - Guodong Zhang 006; Yuanhao Wang 001 (AISTATS, 2021)

Online Learning in Unknown Markov Games. - Yi Tian; Yuanhao Wang 001; Tiancheng Yu; Suvrit Sra (ICML, 2021)

An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap. - Yuanhao Wang 001; Ruosong Wang; Sham M. Kakade (NeurIPS, 2021)

Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. - Guodong Zhang 006; Yuanhao Wang 001; Laurent Lessard; Roger B. Grosse (CoRR, 2021)

An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap. - Yuanhao Wang 001; Ruosong Wang; Sham M. Kakade (CoRR, 2021)

V-Learning - A Simple, Efficient, Decentralized Algorithm for Multiagent RL. - Chi Jin 001; Qinghua Liu; Yuanhao Wang 001; Tiancheng Yu (CoRR, 2021)

Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization. - Guodong Zhang 006; Yuanhao Wang 001; Laurent Lessard; Roger B. Grosse (AISTATS, 2022)

Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits. - Qinghua Liu; Yuanhao Wang 001; Chi Jin 001 (ICML, 2022)

Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation. - Yuanhao Wang 001; Qinghua Liu; Yu Bai 017; Chi Jin 001 (COLT, 2023)

Learning Rationalizable Equilibria in Multiplayer Games. - Yuanhao Wang 001; Dingwen Kong; Yu Bai 017; Chi Jin 001 (ICLR, 2023)

Is RLHF More Difficult than Standard RL? A Theoretical Perspective. - Yuanhao Wang 001; Qinghua Liu; Chi Jin 001 (NeurIPS, 2023)

Is RLHF More Difficult than Standard RL? - Yuanhao Wang 001; Qinghua Liu; Chi Jin 001 (CoRR, 2023)

Directional Smoothness and Gradient Methods: Convergence and Adaptivity. - Aaron Mishkin; Ahmed Khaled 001; Yuanhao Wang 001; Aaron Defazio; Robert M. Gower (NeurIPS, 2024)

Towards Principled Superhuman AI for Multiplayer Symmetric Games. - Jiawei Ge 003; Yuanhao Wang 001; Wenzhe Li; Chi Jin 001 (CoRR, 2024)

V-Learning - A Simple, Efficient, Decentralized Algorithm for Multiagent Reinforcement Learning. - Chi Jin 001; Qinghua Liu; Yuanhao Wang 001; Tiancheng Yu (Math. Oper. Res., 2024)

Is Elo Rating Reliable? A Study Under Model Misspecification. - Shange Tang; Yuanhao Wang 001; Chi Jin 001 (CoRR, 2025)

Tie-breaking Agnostic Lower Bound for Fictitious Play. - Yuanhao Wang 001 (CoRR, 2025)