Junghyun Lee
Junghyun Lee
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Bandits
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
We present a unified likelihood ratio-based confidence sequence (CS) for any (self-concordant) generalized linear model (GLM) that is …
Junghyun Lee
,
Se-Young Yun
,
Kwang-Sung Jun
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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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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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Collaborative Multi-Agent Bandits
specific topics tbd
Junghyun Lee
Theoretical Analyses of Reinforcement Learning with Human Feedback (RLHF) and Related Problems
(tbd)
Junghyun Lee
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