| Provably Efficient Reinforcement Learning with Linear Function Approximation C Jin, Z Yang, Z Wang, MI Jordan Mathematics of Operations Research/Annual Conference on Learning Theory, 2022 | 786 | 2022 |
| A Theoretical Analysis of Deep Q-Learning J Fan, Z Wang, Y Xie, Z Yang Learning for Dynamics and Control, 2020 | 726 | 2020 |
| Is Pessimism Provably Efficient for Offline RL? Y Jin, Z Yang, Z Wang International Conference on Machine Learning, 2021 | 390 | 2021 |
| Provably Efficient Exploration in Policy Optimization Q Cai, Z Yang, C Jin, Z Wang International Conference on Machine Learning, 2020 | 296 | 2020 |
| A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic M Hong, HT Wai, Z Wang, Z Yang SIAM Journal on Optimization, 2022 | 257* | 2022 |
| Neural Policy Gradient Methods: Global Optimality and Rates of Convergence L Wang, Q Cai, Z Yang, Z Wang International Conference on Learning Representations, 2020 | 247 | 2020 |
| Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy B Liu, Q Cai, Z Yang, Z Wang Advances in Neural Information Processing Systems, 2019 | 208* | 2019 |
| Optimal Computational and Statistical Rates of Convergence for Sparse Nonconvex Learning Problems Z Wang, H Liu, T Zhang Annals of Statistics, 2014 | 205 | 2014 |
| Multi-Agent Reinforcement Learning via Double-Averaging Primal-Dual Optimization HT Wai, Z Yang, Z Wang, M Hong Advances in Neural Information Processing Systems, 2018 | 197 | 2018 |
| A Strictly Contractive Peaceman--Rachford Splitting Method for Convex Programming B He, H Liu, Z Wang, X Yuan SIAM Journal on Optimization, 2014 | 195 | 2014 |
| A Nonconvex Optimization Framework for Low Rank Matrix Estimation T Zhao, Z Wang, H Liu Advances in Neural Information Processing Systems, 2015 | 192* | 2015 |
| Provably Efficient Safe Exploration via Primal-Dual Policy Optimization D Ding, X Wei, Z Yang, Z Wang, MR Jovanović International Conference on Artificial Intelligence and Statistics, 2021 | 161 | 2021 |
| Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima Q Cai, Z Yang, JD Lee, Z Wang Mathematics of Operations Research/Advances in Neural Information Processing …, 2019 | 148* | 2019 |
| Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium Q Xie, Y Chen, Z Wang, Z Yang Mathematics of Operations Research/Annual Conference on Learning Theory, 2022 | 146 | 2022 |
| On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost Z Yang, Y Chen, M Hong, Z Wang Advances in Neural Information Processing Systems, 2019 | 138 | 2019 |
| Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations Z Yang, C Jin, Z Wang, M Wang, MI Jordan Advances in Neural Information Processing Systems, 2020 | 135* | 2020 |
| High-Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality Z Wang, Q Gu, Y Ning, H Liu Advances in Neural Information Processing Systems, 2015 | 134 | 2015 |
| A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum P Khanduri, S Zeng, M Hong, HT Wai, Z Wang, Z Yang Advances in Neural Information Processing Systems, 2021 | 128 | 2021 |
| Convergent Policy Optimization for Safe Reinforcement Learning M Yu, Z Yang, M Kolar, Z Wang Advances in Neural Information Processing Systems, 2019 | 121 | 2019 |
| Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning C Bai, L Wang, Z Yang, Z Deng, A Garg, P Liu, Z Wang International Conference on Learning Representations, 2022 | 120 | 2022 |
