1 citations · 2 across the 15 of their papers we have counts for
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Exploration via Feature Perturbation in Contextual Bandits
Seouh-won Yi, Min-hwan Oh
We propose feature perturbation, a simple yet effective exploration strategy for contextual bandits that injects randomness directly into feature inputs, instead of randomizing unk…
Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
Joongkyu Lee, Seouh-won Yi, Min-hwan Oh
We study online preference-based reinforcement learning (PbRL) with the goal of improving sample efficiency. While a growing body of theoretical work has emerged-motivated by PbRL'…
Combinatorial Reinforcement Learning with Preference Feedback
Joongkyu Lee, Min-hwan Oh
In this paper, we consider combinatorial reinforcement learning with preference feedback, where a learning agent sequentially offers an action--an assortment of multiple items to--…
Improved Online Confidence Bounds for Multinomial Logistic Bandits
Joongkyu Lee, Min-hwan Oh
In this paper, we propose an improved online confidence bound for multinomial logistic (MNL) models and apply this result to MNL bandits, achieving variance-dependent optimal regre…