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Openings I am actively seeking self-motivated students with strong mathematical or programming skills for the following positions:
If you have backgrounds in or are passionate about applied or theoretical aspects of:
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Research Interest My recent research interests generally focus on sequential decision-making problems and their applications in Embodied AI. Specifically, my research interest include both applications and theories of reinforcement learning, game theory, nonconvex optimization, large language models (LLMs), robotics, generative models (e.g. diffusion models), and econometrics. |
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Selected Recent Publication [Full List] [Multi-Objective RL] Traversing
Pareto Optimal Policies: Provably Efficient Multi-Objective
Reinforcement Learning
(Preprint) [PDF] [Robust LLM] ROPO: Robust Preference Optimization for Large Language Models (Preprint) [PDF] [RL & Econ] Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach Journal of Machine Learning Research (JMLR), 2024 [PDF] [Multi-Objective LLM] Rewards-in-Context: Multi-Objective Alignment of Foundation Models with Dynamic Preference Adjustment International Conference on Machine Learning (ICML), 2024 [PDF] [Code] [Risk-Sensitive RL] Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning International Conference on Machine Learning (ICML spotlight), 2024 [PDF]
[Multi-Objective LLM]
Arithmetic Control of LLMs for Diverse User Preferences:
Directional Preference Alignment with Multi-Objective
Rewards
Annual Meeting of the Association for Computational Linguistics (ACL main), 2024 [PDF] [Code] [RL & Game] Posterior Sampling for Competitive RL: Function Approximation and Partial Observation Advances in Neural Information Processing Systems (NeurIPS), 2023 [PDF] [RL] Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics International Conference on Learning Representations (ICLR), 2023 [PDF] [Optimization] Gradient-Variation Bound for Online Convex Optimization with Constraints AAAI Conference on Artificial Intelligence (AAAI), 2023 [PDF] [RL & Game] Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning International Conference on Machine Learning (ICML), 2022 [PDF] [Code] [Optimization] In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle International Conference on Management of Data (SIGMOD), 2022 [PDF] [Extended Version] [RL & Game] On Reward-Free RL
with Kernel and Neural Function Approximations:
Single-Agent MDP and Markov Game
International Conference on Machine Learning (ICML), 2021 [PDF] [RL & Game] Provably
Efficient Fictitious Play Policy Optimization for Zero-Sum
Markov Games with Structured Transitions
International Conference on Machine Learning (ICML), 2021 [PDF] [Image Rendering] Stylized Neural Painting IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 [PDF] [Code] [Project]
[Safe RL] Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss Advances in Neural Information Processing Systems (NeurIPS), 2020 [PDF]
[Compressed Sensing] Robust
One-Bit Recovery via ReLU Generative Networks:
Near-Optimal Statistical Rate and Global Landscape
Analysis
International Conference on Machine Learning (ICML), 2020 [PDF] |
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Grant
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Academic Service Conference
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