Online Learning

Looking Through the Mirror: Minimax-Optimal Regularized Regrets in Online Learning and Bandits
We revisit regularized regret minimization under full-information and bandit feedback, where a learner optimizes an objective of the …
Junghyun Lee, Yujun Kim, Chulhee Yun, Se-Young Yun
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A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
We consider the problem of heteroskedastic generalized linear bandits (GLBs) with adversarial corruptions, which subsumes various …
Sanghwa Kim, Junghyun Lee, Se-Young Yun
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A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
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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Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion