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Block Optimism for Nonstationary Bandits with Latent Linear Dynamics
Taehyun Hwang, Hyunjun Choi, Heesang Ann +1
We study an endogenous nonstationary stochastic bandit problem with latent linear dynamics, where actions affect both immediate rewards and the future evolution of an unobserved la…
Linear Ensemble Sampling with Smaller Ensembles
Taehyun Hwang, Min-hwan Oh
Ensemble sampling offers a practical approach to randomized exploration by maintaining a collection of models, but how small an ensemble can be while retaining strong regret guaran…
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…
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…
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…