| Fully decentralized multi-agent reinforcement learning with networked agents K Zhang, Z Yang, H Liu, T Zhang, T Basar International Conference on Machine Learning, 5872-5881, 2018 | 265 | 2018 |
| Multi-agent reinforcement learning: A selective overview of theories and algorithms K Zhang, Z Yang, T Başar Handbook of Reinforcement Learning and Control, 321-384, 2021 | 237 | 2021 |
| Provably efficient reinforcement learning with linear function approximation C Jin, Z Yang, Z Wang, MI Jordan Conference on Learning Theory, 2137-2143, 2020 | 179 | 2020 |
| A theoretical analysis of deep Q-learning J Fan, Z Wang, Y Xie, Z Yang Learning for Dynamics and Control, 486-489, 2020 | 166 | 2020 |
| Multi-agent reinforcement learning via double averaging primal-dual optimization HT Wai, Z Yang, Z Wang, M Hong arXiv preprint arXiv:1806.00877, 2018 | 104 | 2018 |
| Provably efficient exploration in policy optimization Q Cai, Z Yang, C Jin, Z Wang International Conference on Machine Learning, 1283-1294, 2020 | 88 | 2020 |
| Neural policy gradient methods: Global optimality and rates of convergence L Wang, Q Cai, Z Yang, Z Wang arXiv preprint arXiv:1909.01150, 2019 | 87 | 2019 |
| Sparse nonlinear regression: Parameter estimation and asymptotic inference Z Yang, Z Wang, H Liu, YC Eldar, T Zhang arXiv preprint arXiv:1511.04514, 2015 | 64* | 2015 |
| Neural proximal/trust region policy optimization attains globally optimal policy B Liu, Q Cai, Z Yang, Z Wang arXiv preprint arXiv:1906.10306, 2019 | 63 | 2019 |
| Policy optimization provably converges to Nash equilibria in zero-sum linear quadratic games K Zhang, Z Yang, T Başar arXiv preprint arXiv:1906.00729, 2019 | 59 | 2019 |
| Neural temporal-difference learning converges to global optima Q Cai, Z Yang, J Lee, Z Wang | 52 | 2019 |
| Networked multi-agent reinforcement learning in continuous spaces K Zhang, Z Yang, T Basar 2018 IEEE conference on decision and control (CDC), 2771-2776, 2018 | 52 | 2018 |
| On semiparametric exponential family graphical models Z Yang, Y Ning, H Liu arXiv preprint arXiv:1412.8697, 2014 | 41* | 2014 |
| Convergent policy optimization for safe reinforcement learning M Yu, Z Yang, M Kolar, Z Wang Advances in Neural Information Processing Systems 32, 3127-3139, 2019 | 40 | 2019 |
| High-dimensional non-Gaussian single index models via thresholded score function estimation Z Yang, K Balasubramanian, H Liu International Conference on Machine Learning, 3851-3860, 2017 | 31 | 2017 |
| Provably global convergence of actor-critic: A case for linear quadratic regulator with ergodic cost Z Yang, Y Chen, M Hong, Z Wang | 29 | 2019 |
| Finite-sample analyses for fully decentralized multi-agent reinforcement learning K Zhang, Z Yang, H Liu, T Zhang, T Basar arXiv preprint arXiv:1812.02783, 2018 | 29 | 2018 |
| Misspecified nonconvex statistical optimization for phase retrieval Z Yang, LF Yang, EX Fang, T Zhao, Z Wang, M Neykov arXiv preprint arXiv:1712.06245, 2017 | 29* | 2017 |
| Neural trust region/proximal policy optimization attains globally optimal policy B Liu, Q Cai, Z Yang, Z Wang | 28 | 2019 |
| Provably efficient safe exploration via primal-dual policy optimization D Ding, X Wei, Z Yang, Z Wang, M Jovanovic International Conference on Artificial Intelligence and Statistics, 3304-3312, 2021 | 27 | 2021 |
