3 papers
stat.ML2026
Generalized Linear Bandits with Memory
Heesang Ann, Hyunjun Choi, Taehyun Hwang +3
We study generalized linear bandits with memory, an endogenous non-stationary setting in which rewards depend on past actions through a finite memory matrix. Building on prior work…
stat.ML2026
Diversified Multinomial Logit Contextual Bandits
Heesang Ann, Taehyun Hwang, Min-hwan Oh
Existing contextual multinomial logit (MNL) bandits model relevance-driven choice but ignore the potential benefits of within-assortment diversity, while submodular/combinatorial b…
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…