3 papers
cs.LG2026
Tractable Multinomial Logit Contextual Bandits with Non-Linear Utilities
Taehyun Hwang, Dahngoon Kim, Min-hwan Oh
We study the multinomial logit (MNL) contextual bandit problem for sequential assortment selection. Although most existing research assumes utility functions to be linear in item f…
stat.ML2025
Thompson Sampling for Multi-Objective Linear Contextual Bandit
Somangchan Park, Heesang Ann, Min-hwan Oh
We study the multi-objective linear contextual bandit problem, where multiple possible conflicting objectives must be optimized simultaneously. We propose \texttt{MOL-TS}, the \tex…
stat.ML2025
Linear Bandits with Partially Observable Features
Wonyoung Kim, Sungwoo Park, Garud Iyengar +2
We study the linear bandit problem that accounts for partially observable features. Without proper handling, unobserved features can lead to linear regret in the decision horizon $…