Junghyun Lee
Junghyun Lee
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Online Learning
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
Event Weekly OSI Lab Seminar Short summary In this seminar, I will talk my recent work on the new, state-of-the-art regret bound for (multinomial) logistic bandits, and the regret-to-confidence-set conversion, the key technical novelty behind such improvement.
Mar 19, 2024
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
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
Logistic bandit is a ubiquitous framework of modeling users’ choices, e.g., click vs. no click for advertisement recommender …
Junghyun Lee
,
Se-Young Yun
,
Kwang-Sung Jun
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Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
Proposes a framework for performing fair PCA in memory limited, streaming setting. Sample complexity results and empirical discussions show the superiority of our approach compared to the existing approaches.
Junghyun Lee
,
Hanseul Cho
,
Se-Young Yun
,
Chulhee Yun
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Project
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Likelihood Loss-based Confidence Sequence
Aim to derive tight likelihood loss-based confidence sequence with time-uniform guarantees, with applications to sequential decision making and RL.
Junghyun Lee
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