| Malib: A parallel framework for population-based multi-agent reinforcement learning M Zhou, Z Wan, H Wang, M Wen, R Wu, Y Wen, Y Yang, Y Yu, J Wang, ... Journal of Machine Learning Research 24 (150), 1-12, 2023 | 42 | 2023 |
| Offline constrained multi-objective reinforcement learning via pessimistic dual value iteration R Wu, Y Zhang, Z Yang, Z Wang Advances in Neural Information Processing Systems 34, 25439-25451, 2021 | 15 | 2021 |
| Making rl with preference-based feedback efficient via randomization R Wu, W Sun arXiv preprint arXiv:2310.14554, 2023 | 13 | 2023 |
| Distributional offline policy evaluation with predictive error guarantees R Wu, M Uehara, W Sun International Conference on Machine Learning, 37685-37712, 2023 | 11 | 2023 |
| The benefits of being distributional: Small-loss bounds for reinforcement learning K Wang, K Zhou, R Wu, N Kallus, W Sun Advances in Neural Information Processing Systems 36, 2023 | 10 | 2023 |
| Selective sampling and imitation learning via online regression A Sekhari, K Sridharan, W Sun, R Wu Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| Contextual bandits and imitation learning via preference-based active queries A Sekhari, K Sridharan, W Sun, R Wu arXiv preprint arXiv:2307.12926, 2023 | 7 | 2023 |
| Contextual bandits and imitation learning with preference-based active queries A Sekhari, K Sridharan, W Sun, R Wu Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics R Wu, A Sekhari, A Krishnamurthy, W Sun arXiv preprint arXiv:2406.11810, 2024 | | 2024 |
