| Sample-optimal parametric q-learning using linearly additive features L Yang, M Wang International conference on machine learning, 6995-7004, 2019 | 440 | 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 | 386 | 2020 |
| Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound L Yang, M Wang International Conference on Machine Learning, 10746-10756, 2020 | 378 | 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 | 313 | 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 | 286 | 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 | 274 | 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 | 270 | 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 | 183 | 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 | 150 | 2020 |
| 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 | 134 | 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 | 122 | 2014 |
| 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 | 101 | 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 | 93 | 2020 |
| Q-learning with logarithmic regret K Yang, L Yang, S Du International Conference on Artificial Intelligence and Statistics, 1576-1584, 2021 | 92 | 2021 |
| 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 | 84 | 2022 |
| Model-based reinforcement learning with value-targeted regression Z Jia, L Yang, C Szepesvari, M Wang Learning for Dynamics and Control, 666-686, 2020 | 83 | 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 | 68 | 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 | 67 | 2017 |
| Don't forget to connect! improving rag with graph-based reranking J Dong, B Fatemi, B Perozzi, LF Yang, A Tsitsulin arXiv preprint arXiv:2405.18414, 2024 | 62 | 2024 |
| Randomized exploration in reinforcement learning with general value function approximation H Ishfaq, Q Cui, V Nguyen, A Ayoub, Z Yang, Z Wang, D Precup, L Yang International Conference on Machine Learning, 4607-4616, 2021 | 62 | 2021 |
Mengdi WangProfessor, Princeton AI Lab, CSML&ECE, Princeton UniversityVerified email at princeton.edu
