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Siliang Zeng
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2020 – today
- 2024
- [i8]Jiaxiang Li, Siliang Zeng, Hoi-To Wai, Chenliang Li, Alfredo García, Mingyi Hong:
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment. CoRR abs/2405.17888 (2024) - [i7]Chenliang Li, Siliang Zeng, Zeyi Liao, Jiaxiang Li, Dongyeop Kang, Alfredo García, Mingyi Hong:
Joint Demonstration and Preference Learning Improves Policy Alignment with Human Feedback. CoRR abs/2406.06874 (2024) - 2023
- [c10]Ran Wei, Siliang Zeng, Chenliang Li, Alfredo García, Anthony D. McDonald, Mingyi Hong:
A Bayesian Approach to Robust Inverse Reinforcement Learning. CoRL 2023: 2304-2322 - [c9]Hao Wang, Siliang Zeng, Zhenwei Xue, Xinglin Zhu, Fei Wu, Ning Wang:
ProfLLM: A framework for adapting offline large language models to few-shot expert knowledge. ICAICE 2023: 708-714 - [c8]Siliang Zeng, Chenliang Li, Alfredo García, Mingyi Hong:
When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning. NeurIPS 2023 - [i6]Siliang Zeng, Chenliang Li, Alfredo García, Mingyi Hong:
Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning. CoRR abs/2302.07457 (2023) - [i5]Ran Wei, Siliang Zeng, Chenliang Li, Alfredo García, Anthony D. McDonald, Mingyi Hong:
A Bayesian Approach to Robust Inverse Reinforcement Learning. CoRR abs/2309.08571 (2023) - 2022
- [j1]Mingyi Hong, Siliang Zeng, Junyu Zhang, Haoran Sun:
On the Divergence of Decentralized Nonconvex Optimization. SIAM J. Optim. 32(4): 2879-2908 (2022) - [c7]Siliang Zeng, Tianyi Chen, Alfredo García, Mingyi Hong:
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees. L4DC 2022: 278-290 - [c6]Songtao Lu, Siliang Zeng, Xiaodong Cui, Mark S. Squillante, Lior Horesh, Brian Kingsbury, Jia Liu, Mingyi Hong:
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization. NeurIPS 2022 - [c5]Siliang Zeng, Chenliang Li, Alfredo García, Mingyi Hong:
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees. NeurIPS 2022 - [i4]Siliang Zeng, Mingyi Hong, Alfredo García:
Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees. CoRR abs/2210.01282 (2022) - 2021
- [c4]Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang:
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum. NeurIPS 2021: 30271-30283 - [i3]Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang:
A Momentum-Assisted Single-Timescale Stochastic Approximation Algorithm for Bilevel Optimization. CoRR abs/2102.07367 (2021) - [i2]Siliang Zeng, Tianyi Chen, Alfredo García, Mingyi Hong:
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees. CoRR abs/2110.05597 (2021) - 2020
- [c3]Qi Cao, Siliang Zeng, Man-On Pun, Yi Chen:
Network-Level System Performance Prediction Using Deep Neural Networks with Cross-Layer Information. ICC 2020: 1-6 - [c2]Siliang Zeng, Xingfei Xu, Yi Chen:
Multi-Agent Reinforcement Learning for Adaptive Routing: A Hybrid Method using Eligibility Traces. ICCA 2020: 1332-1339 - [i1]Mingyi Hong, Siliang Zeng, Junyu Zhang, Haoran Sun:
On the Divergence of Decentralized Non-Convex Optimization. CoRR abs/2006.11662 (2020)
2000 – 2009
- 2006
- [c1]Jianbo Xu, Siliang Zeng, Fengjiao Qu:
A new In-network data aggregation technology of wireless sensor networks. SKG 2006: 104
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