- 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)