6 papers
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…
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…
Block-Sphere Vector Quantization
Heesang Ann, Joongkyu Lee, Min-hwan Oh
Vector quantization is a fundamental primitive for scalable machine learning systems, enabling memory-efficient storage, fast retrieval, and compressed inference. Recent rotation-b…
Blessings of Multiple Good Arms in Multi-Objective Linear Bandits
Heesang Ann, Min-hwan Oh
The multi objective bandit setting has traditionally been regarded as more complex than the single objective case, as multiple objectives must be optimized simultaneously. In contr…
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…