3 papers
stat.ML2026
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
Sanghoon Yu, Min-hwan Oh
We study linear contextual bandits under rare parameter updates: the learner may incorporate reward feedback into its parameter estimate only at a small number of update times, whi…
stat.ML2025
Experimental Design for Semiparametric Bandits
Seok-Jin Kim, Gi-Soo Kim, Min-hwan Oh
We study finite-armed semiparametric bandits, where each arm's reward combines a linear component with an unknown, potentially adversarial shift. This model strictly generalizes cl…
stat.ML2022
Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits
Wonyoung Kim, Myunghee Cho Paik, Min-hwan Oh
We propose a linear contextual bandit algorithm with regret bound, where is the dimension of contexts and isthe time horizon. Our proposed algorithm is…