Maximum-likelihood inverse reinforcement learning with finite-time guarantees S Zeng, C Li, A Garcia, M Hong Advances in Neural Information Processing Systems 35, 10122-10135, 2022 | 25 | 2022 |
Understanding expertise through demonstrations: A maximum likelihood framework for offline inverse reinforcement learning S Zeng, C Li, A Garcia, M Hong arXiv preprint arXiv:2302.07457, 2023 | 8 | 2023 |
A bayesian approach to robust inverse reinforcement learning R Wei, S Zeng, C Li, A Garcia, AD McDonald, M Hong Conference on Robot Learning, 2304-2322, 2023 | 3 | 2023 |
When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning S Zeng, C Li, A Garcia, M Hong Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
Robust inverse reinforcement learning through bayesian theory of mind R Wei, S Zeng, C Li, A Garcia, A McDonald, M Hong First Workshop on Theory of Mind in Communicating Agents, 2023 | 1 | 2023 |
Joint Demonstration and Preference Learning Improves Policy Alignment with Human Feedback C Li, S Zeng, Z Liao, J Li, D Kang, A Garcia, M Hong arXiv preprint arXiv:2406.06874, 2024 | | 2024 |
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment J Li, S Zeng, HT Wai, C Li, A Garcia, M Hong arXiv preprint arXiv:2405.17888, 2024 | | 2024 |
Transformer Based Approach for Wireless Resource Allocation Problems Involving Mixed Discrete and Continuous Variables B Song, Z Zhou, C Li, D Guo, X Fu, M Hong 2023 IEEE 24th International Workshop on Signal Processing Advances in …, 2023 | | 2023 |