From the 1 of 4 linked papers with an AI index.
4 papers
Diversified Multinomial Logit Contextual Bandits
Heesang Ann, Taehyun Hwang, Min-hwan Oh
The paper introduces a diversified multinomial logit (DMNL) contextual bandit model that combines relevance-driven choice with a submodular diversity term, and proposes a white‑box…
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…