3 papers
stat.ML2025
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…
stat.ML2025
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…
stat.ML2025
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…