A new In-network data aggregation technology of wireless sensor networks. - Jianbo Xu; Siliang Zeng; Fengjiao Qu (SKG, 2006)

Network-Level System Performance Prediction Using Deep Neural Networks with Cross-Layer Information. - Qi Cao 001; Siliang Zeng; Man-On Pun; Yi Chen 013 (ICC, 2020)

Multi-Agent Reinforcement Learning for Adaptive Routing: A Hybrid Method using Eligibility Traces. - Siliang Zeng; Xingfei Xu; Yi Chen 013 (ICCA, 2020)

On the Divergence of Decentralized Non-Convex Optimization. - Mingyi Hong 001; Siliang Zeng; Junyu Zhang; Haoran Sun (CoRR, 2020)

A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum. - Prashant Khanduri; Siliang Zeng; Mingyi Hong 001; Hoi-To Wai; Zhaoran Wang 001; Zhuoran Yang (NeurIPS, 2021)

A Momentum-Assisted Single-Timescale Stochastic Approximation Algorithm for Bilevel Optimization. - Prashant Khanduri; Siliang Zeng; Mingyi Hong 001; Hoi-To Wai; Zhaoran Wang 001; Zhuoran Yang (CoRR, 2021)

Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees. - Siliang Zeng; Tianyi Chen; Alfredo García 001; Mingyi Hong 001 (L4DC, 2022)

A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization. - Songtao Lu; Siliang Zeng; Xiaodong Cui; Mark S. Squillante; Lior Horesh; Brian Kingsbury; Jia Liu 002; Mingyi Hong 001 (NeurIPS, 2022)

Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees. - Siliang Zeng; Chenliang Li; Alfredo García 001; Mingyi Hong 001 (NeurIPS, 2022)

On the Divergence of Decentralized Nonconvex Optimization. - Mingyi Hong 001; Siliang Zeng; Junyu Zhang; Haoran Sun (SIAM J. Optim., 2022)

A Bayesian Approach to Robust Inverse Reinforcement Learning. - Ran Wei; Siliang Zeng; Chenliang Li; Alfredo García 001; Anthony D. McDonald; Mingyi Hong 001 (CoRL, 2023)

ProfLLM: A framework for adapting offline large language models to few-shot expert knowledge. - Hao Wang 204; Siliang Zeng; Zhenwei Xue; Xinglin Zhu; Fei Wu; Ning Wang 079 (ICAICE, 2023)

When Demonstrations meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning. - Siliang Zeng; Chenliang Li; Alfredo García 001; Mingyi Hong 001 (NeurIPS, 2023)

Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning. - Siliang Zeng; Chenliang Li; Alfredo García 001; Mingyi Hong 001 (CoRR, 2023)

Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment. - Jiaxiang Li; Siliang Zeng; Hoi-To Wai; Chenliang Li; Alfredo García 001; Mingyi Hong 001 (NeurIPS, 2024)

Joint Demonstration and Preference Learning Improves Policy Alignment with Human Feedback. - Chenliang Li; Siliang Zeng; Zeyi Liao; Jiaxiang Li; Dongyeop Kang; Alfredo García 001; Mingyi Hong 001 (CoRR, 2024)

Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality. - Ruijia Zhang; Siliang Zeng; Chenliang Li; Alfredo García 001; Mingyi Hong 001 (AISTATS, 2025)

Joint Reward and Policy Learning with Demonstrations and Human Feedback Improves Alignment. - Chenliang Li; Siliang Zeng; Zeyi Liao; Jiaxiang Li; Dongyeop Kang; Alfredo García 001; Mingyi Hong 001 (ICLR, 2025)

Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees. - Siliang Zeng; Mingyi Hong 001; Alfredo García 001 (Oper. Res., 2025)

Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens. - Zhepeng Cen; Yao Liu 009; Siliang Zeng; Pratik Chaudhari; Huzefa Rangwala; George Karypis; Rasool Fakoor (Trans. Mach. Learn. Res., 2025)

From Demonstrations to Rewards: Alignment Without Explicit Human Preferences. - Siliang Zeng; Yao Liu 009; Huzefa Rangwala; George Karypis; Mingyi Hong 001; Rasool Fakoor (CoRR, 2025)

Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment. - Siliang Zeng; Quan Wei 001; William Brown; Oana Frunza; Yuriy Nevmyvaka; Mingyi Hong 001 (CoRR, 2025)

Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach. - Xinnan Zhang; Chenliang Li; Siliang Zeng; Jiaxiang Li; Zhongruo Wang; Kaixiang Lin; Songtao Lu; Alfredo García 001; Mingyi Hong 001 (CoRR, 2025)