A near-optimal algorithm for stochastic bilevel optimization via double-momentum P Khanduri, S Zeng, M Hong, HT Wai, Z Wang, Z Yang Advances in neural information processing systems 34, 30271-30283, 2021 | 114 | 2021 |
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 |
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees S Zeng, T Chen, A Garcia, M Hong 4th Annual Learning for Dynamics & Control Conference (L4DC 2022), 2021 | 14 | 2021 |
A stochastic linearized augmented lagrangian method for decentralized bilevel optimization S Lu, S Zeng, X Cui, M Squillante, L Horesh, B Kingsbury, J Liu, M Hong Advances in Neural Information Processing Systems 35, 30638-30650, 2022 | 13 | 2022 |
A momentum-assisted single-timescale stochastic approximation algorithm for bilevel optimization P Khanduri, S Zeng, M Hong, HT Wai, Z Wang, Z Yang arXiv preprint arXiv:2102.07367, 2021 | 12 | 2021 |
Multi-Agent Reinforcement Learning for Adaptive Routing: A Hybrid Method using Eligibility Traces S Zeng, X Xu, Y Chen 2020 IEEE 16th International Conference on Control & Automation (ICCA), 1332 …, 2020 | 11 | 2020 |
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 | 10* | 2024 |
On the divergence of decentralized nonconvex optimization M Hong, S Zeng, J Zhang, H Sun SIAM Journal on Optimization 32 (4), 2879-2908, 2022 | 10 | 2022 |
Structural estimation of markov decision processes in high-dimensional state space with finite-time guarantees S Zeng, M Hong, A Garcia arXiv preprint arXiv:2210.01282, 2022 | 8 | 2022 |
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 | 4* | 2023 |
Network-Level System Performance Prediction Using Deep Neural Networks with Cross-Layer Information Q Cao, S Zeng, MO Pun, Y Chen ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-6, 2020 | 1 | 2020 |
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 |