5 papers
Lipschitz Bandits with Stochastic Delayed Feedback
Zhongxuan Liu, Yue Kang, Thomas C. M. Lee
The Lipschitz bandit problem extends stochastic bandits to a continuous action set defined over a metric space, where the expected reward function satisfies a Lipschitz condition.…
Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions
Yue Kang, Mingshuo Liu, Bongsoo Yi +4
Generalized linear bandits have been extensively studied due to their broad applicability in real-world online decision-making problems. However, these methods typically assume tha…
Quantum Lipschitz Bandits
Bongsoo Yi, Yue Kang, Yao Li
The Lipschitz bandit is a key variant of stochastic bandit problems where the expected reward function satisfies a Lipschitz condition with respect to an arm metric space. With its…
Generalized Low-Rank Matrix Contextual Bandits with Graph Information
Yao Wang, Jiannan Li, Yue Kang +2
The matrix contextual bandit (CB), as an extension of the well-known multi-armed bandit, is a powerful framework that has been widely applied in sequential decision-making scenario…
Biased Dueling Bandits with Stochastic Delayed Feedback
Bongsoo Yi, Yue Kang, Yao Li
The dueling bandit problem, an essential variation of the traditional multi-armed bandit problem, has become significantly prominent recently due to its broad applications in onlin…