- Joint Reward and Policy Learning with Demonstrations and Human Feedback Improves Alignment, Ch. Li, S. Zeng, Z. Liao, J. Li, D. Kang, A. Garcia, M. Hong, The Thirteenth International Conference on Learning Representations (ICLR) (2025)
- Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality, R. Zhang, S. Zeng, Ch. Li, A. Garcia, M. Hong, Proceedings of 28th International Conference on Artificial Intelligence and Statistics (AISTATS) (2025)
- Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment, J. Li, S. Zeng, H. Wai, Ch. Li, A. Garcia and M. Hong, Proceedings of Neural Information Processing Systems (NeurIPS) (2024)
- Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning, S. Zeng, Ch. Li, A. Garcia and Hong, M. Proceedings of Neural Information Processing Systems (NeurIPS) (2023)
- A Bayesian Approach to Robust Inverse Reinforcement Learning, R. Wei, S. Zeng, Ch. Li, A. Garcia, A. McDonald, M. Hong. Proceedings of 7th Conference on Robot Learning (CoRL 2023)
- Maximum Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees, S. Zeng, Ch. Li, A. Garcia and Hong, M. Proceedings of Neural Information Processing Systems (NeurIPS) (2022)
- World Model Learning From Demonstrations With Active Inference: Application to Driving Behavior, R. Wei, A. Garcia, A. McDonald, G. Markkula, J. Engstrom, I. Supeene and M. O’Kelly, 3rd International Workshop on Active Inference (IWAI), 2022
- Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees, S. Zeng, T. Chen, A. Garcia and Hong, M. Proceedings of Machine Learning Research (2022) vol 168, pp. 1-45
- Decentralized Riemannian Gradient Descent on the Stiefel Manifold, S. Chen, A.Garcia. M. Hong and S. Shahrampour, Proceedings of International Conference on Machine Learning ICML (2021)