Junghyun Lee
Junghyun Lee
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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
A Primer on (Combinatorial Semi-) Bandits
Event Weekly OSI Lab Seminar Short summary In this seminar, I start by introducing the problem of bandits, overall proof techniques for the fundamental lower bounds, and a brief overview of variants of bandits.
Sep 21, 2023
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
A Primer on (Combinatorial) Bandits
Event Weekly OptiML Lab Group Meeting Short summary In this seminar, I will first introduce bandits, then move on to talk about a special class of bandits, called combinatorial (semi-)bandits, and even more specific example of it, namely, matroid bandits.
Feb 15, 2023
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
From Generalized Linear Bandit to Logistic Bandit: An Overview
Event Weekly OSI Lab Seminar Short summary In this seminar, I will talk about the generalized linear bandits and logistic bandits. Especially for the logistic bandits, I will talk about its connection to the generalized linear bandits and some recent progresses on it from Criteo AI Lab.
Jul 22, 2022
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
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks
A novel problem setting where heterogeneous multi-agent bandits collaborate over a network to minimize their group regret. To deal with the high communication complexity of the classic flooding protocol combined with UCB, a new network protocol called Flooding with Absorption (FwA) is proposed. Theoretical and empirical analyses are provded for flooding and FwA, showing the efficacy of our proposed FwA.
Junghyun Lee
,
Laura Schmid
,
Se-Young Yun
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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
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