Junghyun Lee
Junghyun Lee
Home
Experiences
Publications
Projects
Posts
Seminars
Contact
Light
Dark
Automatic
MDP
Introduction to Reinforcement Learning with Human Feedback (RLHF): A Theoretically Biased Overview
Event Weekly OptiML Lab Group Meeting Short summary In this talk, I will first (somewhat rigorously) introduce the framework of reinforcement learning with human feedback (RLHF). Then I will go over three recent breakthroughs in the analysis and improvement of RLHF.
Nov 30, 2023
Project
Introduction to Reinforcement Learning with Human Feedback (RLHF): A Theoretically Biased Overview
Event Weekly OSI Lab Seminar Short summary In this talk, I will first (somewhat rigorously) introduce the framework of reinforcement learning with human feedback (RLHF). Then I will go over three recent breakthroughs in the analysis and improvement of RLHF.
Nov 30, 2023
Project
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs
Event Weekly OSI Lab Seminar Short summary In this seminar, I will talk about the paper “Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs” (Cheng et al., ICLR 2023).
Mar 31, 2023
Project
Nearly Optimal Latent State Decoding in Block MDPs
Event Weekly OptiML Lab Group Meeting Short summary In this seminar, I will talk about my own paper “Nearly Optimal Latent State Decoding in Block MDPs” (Jedra et al., arXiv 2022).
Oct 7, 2022
Project
Clustering in Block Markov Chains
Event Weekly OSI Lab Seminar Short summary In this seminar, I will talk about the paper “Clustering in Block Markov Chains” (Sanders et al., Ann. Stat. 2020). Abstract (taken directly from the paper)
Nov 26, 2021
Project
Empirical Analyses of Corruption in the Clustering of Block MDPs
We show that a simple trick of randomly corrupting the trajectories in Block MDPs allow for us to use the the clustering algorithm proposed of Jedra et al. (2023) for general classes of MDPs.
Junghyun Lee
,
Se-Young Yun
Cite
Project
Nearly Optimal Latent State Decoding in Block MDPs
First theoretical analysis of model estimation and reward-free RL of block MDP, without resorting to function approximation frameworks. Lower bounds and algorithms with near-optimal upper bound are provided.
Yassir Jedra
,
Junghyun Lee
,
Alexandre Proutière
,
Se-Young Yun
PDF
Cite
Code
Project
Poster
Slides
Preliminary Empirical Analyses of Clustering in Block MDPs
We empirically validate the clustering algorithm proposed in (Jedra et al., 2022).
Junghyun Lee
,
Se-Young Yun
PDF
Cite
Project
Slides
Sample Complexity of Learning in Structured Markov Chains and MDPs
(tbd)
Junghyun Lee
Theoretical Analyses of Reinforcement Learning with Human Feedback (RLHF)
(tbd)
Junghyun Lee
Cite
×