Siliang Zeng
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About me
I am a fourth year PhD student at University of Minnesota, advised by Prof. Mingyi Hong.
Before moving to Minnesota, I received the B.S. in Statistics from The Chinese University of Hong Kong, Shenzhen in 2020.
In my research, I am always thinking about how to design practical algorithms and systems for sequential decision making under uncertainty.
Experience
Applied Scientist Intern, Amazon Web Search (AWS) AI Research and Education lab, Santa Clara, May 2023 - Sep 2023.
Work on finetuning large language model with reinforcement leraning. (Mentor: Kaixiang Lin)
Representative / Recent Works (* indicates equal contribution.)
Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Siliang Zeng*, Chenliang Li*, Alfredo Garcia, Mingyi Hong
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023). (Oral: 0.5% [67/12343] )
Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
Siliang Zeng, Mingyi Hong, Alfredo Garcia
Under major revision at Operations Research.
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong
Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022).
(A previous version accepted by Decision Awareness in Reinforcement Learning Workshop at ICML 2022)
Recent News
Oct 2023: I am thrilled to receive the NeurIPS 2023 Scholar Award. See you in New Orleans!
Sep 2023: One paper has been accepted to NeurIPS 2023 as an oral:
Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning.
August 2023: One paper has been accepted to Conference on Robot Learning (CoRL) 2023:
A Bayesian Approach to Robust Inverse Reinforcement Learning.
May 2023: I join Amazon Web Search (AWS) as an applied scientist intern. I will work on reinforcement learning and large language model training.
March 2023: I gave an invited talk at Amazon AWS about our recent work on offline inverse reinforcement learning:
Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Oct 2022: I am thrilled to receive the NeurIPS 2022 Scholar Award. See you in New Orleans!
Sep 2022: Two papers have been accepted to NeurIPS 2022:
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees.
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization.
Jul 2022: I have been selected for a travel grant from the DARL Workshop at ICML 2022!
Jun 2022: One paper has been accepted to Decision Awareness in Reinforcement Learning Workshop at ICML 2022:
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees.
Jun 2022: One paper has been accepted to SIAM Journal on Optimization:
On the Divergence of Decentralized Non-Convex Optimization.
Mar 2022: One paper has been accepted to L4DC 2022:
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees.
Sep 2021: One paper has been accepted to NeurIPS 2021:
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum.