| Settling the sample complexity of model-based offline reinforcement learning G Li, L Shi, Y Chen, Y Chi, Y Wei The Annals of Statistics 52 (1), 233-260, 2024 | 143 | 2024 |
| Pessimistic Q-learning for offline reinforcement learning: Towards optimal sample complexity L Shi, G Li, Y Wei, Y Chen, Y Chi International Conference on Machine Learning (ICML), 2022, 2022 | 140 | 2022 |
| Distributionally robust model-based offline reinforcement learning with near-optimal sample complexity L Shi, Y Chi Journal of Machine Learning Research 25 (200), 1-91, 2024 | 116 | 2024 |
| Micro hand gesture recognition system using ultrasonic active sensing Y Sang, L Shi, Y Liu IEEE Access 6, 49339-49347, 2018 | 103 | 2018 |
| Breaking the sample complexity barrier to regret-optimal model-free reinforcement learning G Li, L Shi, Y Chen, Y Chi Information and Inference: A Journal of the IMA; Short version as NeurIPS …, 2023 | 90 | 2023 |
| The curious price of distributional robustness in reinforcement learning with a generative model L Shi, G Li, Y Wei, Y Chen, M Geist, Y Chi Advances in Neural Information Processing Systems 36, 79903-79917, 2023 | 76 | 2023 |
| Seeing is not believing: Robust reinforcement learning against spurious correlation W Ding*, L Shi*, Y Chi, D Zhao Advances in Neural Information Processing Systems 36, 66328-66363, 2023 | 50 | 2023 |
| Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation P Huang, M Xu, J Zhu, L Shi, F Fang, D Zhao Conference on Neural Information Processing Systems (NeurIPS), 2022, 2022 | 47 | 2022 |
| Manifold gradient descent solves multi-channel sparse blind deconvolution provably and efficiently L Shi, Y Chi IEEE Transactions on Information Theory 67 (7), 4784-4811, 2021 | 34 | 2021 |
| Device-free multiple people localization through floor vibration L Shi, M Mirshekari, J Fagert, Y Chi, HY Noh, P Zhang, S Pan Proceedings of the 1st ACM International Workshop on Device-Free Human …, 2019 | 32 | 2019 |
| Sample complexity of offline distributionally robust linear markov decision processes H Wang, L Shi, Y Chi arXiv preprint arXiv:2403.12946, 2024 | 27 | 2024 |
| Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty L Shi, E Mazumdar, Y Chi, A Wierman International Conference on Machine Learning (ICML), 2024, 2024 | 22 | 2024 |
| Federated offline reinforcement learning: Collaborative single-policy coverage suffices J Woo, L Shi, G Joshi, Y Chi International Conference on Machine Learning, 2024 | 22 | 2024 |
| A trajectory is worth three sentences: multimodal transformer for offline reinforcement learning Y Wang, M Xu, L Shi, Y Chi Uncertainty in Artificial Intelligence, 2226-2236, 2023 | 15 | 2023 |
| Distributionally robust constrained reinforcement learning under strong duality Z Zhang, K Panaganti, L Shi, Y Sui, A Wierman, Y Yue arXiv preprint arXiv:2406.15788, 2024 | 14 | 2024 |
| Tractable multi-agent reinforcement learning through behavioral economics E Mazumdar, K Panaganti, L Shi The Thirteenth International Conference on Learning Representations, 2025 | 12* | 2025 |
| Robust gymnasium: A unified modular benchmark for robust reinforcement learning S Gu*, L Shi*, M Wen, M Jin, E Mazumdar, Y Chi, A Wierman, C Spanos arXiv preprint arXiv:2502.19652, 2025 | 11 | 2025 |
| Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization C Lu, L Shi, Z Chen, C Wu, A Wierman arXiv preprint arXiv:2411.07591, 2024 | 11 | 2024 |
| MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning Y Liang, J Qiu, W Ding, Z Liu, J Tompkin, M Xu, M Xia, Z Tu, L Shi, J Zhu arXiv preprint arXiv:2505.24871, 2025 | 10 | 2025 |
| Enhancing efficiency of safe reinforcement learning via sample manipulation S Gu*, L Shi*, Y Ding, A Knoll, CJ Spanos, A Wierman, M Jin Advances in Neural Information Processing Systems 37, 17247-17285, 2024 | 10 | 2024 |
Yuejie ChiCharles C. and Dorothea S. Dilley Professor, Yale UniversityVerified email at cmu.edu
