| Sample-optimal parametric q-learning using linearly additive features L Yang, M Wang International conference on machine learning, 6995-7004, 2019 | 383 | 2019 |
| Model-based reinforcement learning with value-targeted regression A Ayoub, Z Jia, C Szepesvari, M Wang, L Yang International Conference on Machine Learning, 463-474, 2020 | 346 | 2020 |
| Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound L Yang, M Wang International Conference on Machine Learning, 10746-10756, 2020 | 339 | 2020 |
| Near-optimal time and sample complexities for solving Markov decision processes with a generative model A Sidford, M Wang, X Wu, L Yang, Y Ye Advances in Neural Information Processing Systems 31, 2018 | 277 | 2018 |
| Is a good representation sufficient for sample efficient reinforcement learning? SS Du, SM Kakade, R Wang, LF Yang arXiv preprint arXiv:1910.03016, 2019 | 252 | 2019 |
| Model-based reinforcement learning with a generative model is minimax optimal A Agarwal, S Kakade, LF Yang Conference on Learning Theory, 67-83, 2020 | 236* | 2020 |
| Reinforcement learning with general value function approximation: Provably efficient approach via bounded eluder dimension R Wang, RR Salakhutdinov, L Yang Advances in Neural Information Processing Systems 33, 6123-6135, 2020 | 185 | 2020 |
| Model-based multi-agent rl in zero-sum markov games with near-optimal sample complexity K Zhang, SM Kakade, T Basar, LF Yang Journal of Machine Learning Research 24 (175), 1-53, 2023 | 158 | 2023 |
| On reward-free reinforcement learning with linear function approximation R Wang, SS Du, L Yang, RR Salakhutdinov Advances in neural information processing systems 33, 17816-17826, 2020 | 132 | 2020 |
| The hierarchical nature of the spin alignment of dark matter haloes in filaments MA Aragon-Calvo, LF Yang Monthly Notices of the Royal Astronomical Society: Letters 440 (1), L46-L50, 2014 | 113 | 2014 |
| Toward the fundamental limits of imitation learning N Rajaraman, L Yang, J Jiao, K Ramchandran Advances in Neural Information Processing Systems 33, 2914-2924, 2020 | 91 | 2020 |
| Solving discounted stochastic two-player games with near-optimal time and sample complexity A Sidford, M Wang, L Yang, Y Ye International Conference on Artificial Intelligence and Statistics, 2992-3002, 2020 | 85 | 2020 |
| Q-learning with logarithmic regret K Yang, L Yang, S Du International Conference on Artificial Intelligence and Statistics, 1576-1584, 2021 | 76 | 2021 |
| Model-based reinforcement learning with value-targeted regression Z Jia, L Yang, C Szepesvari, M Wang Learning for Dynamics and Control, 666-686, 2020 | 73 | 2020 |
| On improving model-free algorithms for decentralized multi-agent reinforcement learning W Mao, L Yang, K Zhang, T Basar International Conference on Machine Learning, 15007-15049, 2022 | 72 | 2022 |
| Preference-based reinforcement learning with finite-time guarantees Y Xu, R Wang, L Yang, A Singh, A Dubrawski Advances in Neural Information Processing Systems 33, 18784-18794, 2020 | 66 | 2020 |
| Provably efficient reinforcement learning with general value function approximation R Wang, R Salakhutdinov, LF Yang arXiv preprint arXiv:2005.10804, 2020 | 65 | 2020 |
| Warmth elevating the depths: shallower voids with warm dark matter LF Yang, MC Neyrinck, MA Aragón-Calvo, B Falck, J Silk Monthly Notices of the Royal Astronomical Society 451 (4), 3606-3614, 2015 | 65 | 2015 |
| Clustering high dimensional dynamic data streams V Braverman, G Frahling, H Lang, C Sohler, LF Yang International Conference on Machine Learning, 576-585, 2017 | 62 | 2017 |
| Feature-based q-learning for two-player stochastic games Z Jia, LF Yang, M Wang arXiv preprint arXiv:1906.00423, 2019 | 60 | 2019 |
Vladimir BravermanProfessor of Computer Science, Johns Hopkins University; Google Research; Adjunct Professor, Rice U.cs.jhu.edu의 이메일 확인됨