5 papers
Oracle-Efficient Combinatorial Semi-Bandits
Jung-hun Kim, Milan VojnoviÄ, Min-hwan Oh
We study the combinatorial semi-bandit problem where an agent selects a subset of base arms and receives individual feedback. While this generalizes the classical multi-armed bandi…
Stochastic Matching Bandits with Rare Optimization Updates
Jung-hun Kim, Min-hwan Oh
We introduce a bandit framework for stochastic matching under the multinomial logit (MNL) choice model. In our setting, agents on one side are assigned to arms on the other…
Optimal and Practical Batched Linear Bandit Algorithm
Sanghoon Yu, Min-hwan Oh
We study the linear bandit problem under limited adaptivity, known as the batched linear bandit. While existing approaches can achieve near-optimal regret in theory, they are often…
Queueing Matching Bandits with Preference Feedback
Jung-hun Kim, Min-hwan Oh
In this study, we consider multi-class multi-server asymmetric queueing systems consisting of queues on one side and servers on the other side, where jobs randomly arrive i…
Dynamic Assortment Selection and Pricing with Censored Preference Feedback
Jung-hun Kim, Min-hwan Oh
In this study, we investigate the problem of dynamic multi-product selection and pricing by introducing a novel framework based on a \textit{censored multinomial logit} (C-MNL) cho…