70 citations · 245 across the 36 of their papers we have counts for
5 papers · 1 filter
Best-of-Both-Worlds Algorithms for Partial Monitoring
Taira Tsuchiya, Shinji Ito, Junya Honda
This study considers the partial monitoring problem with -actions and -outcomes and provides the first best-of-both-worlds algorithms, whose regrets are favorably bounded bot…
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds
Shinji Ito, Taira Tsuchiya, Junya Honda
This paper considers the multi-armed bandit (MAB) problem and provides a new best-of-both-worlds (BOBW) algorithm that works nearly optimally in both stochastic and adversarial set…
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification
Junpei Komiyama, Taira Tsuchiya, Junya Honda
We consider the fixed-budget best arm identification problem where the goal is to find the arm of the largest mean with a fixed number of samples. It is known that the probability…
The Survival Bandit Problem
Charles Riou, Junya Honda, Masashi Sugiyama
We introduce and study a new variant of the multi-armed bandit problem (MAB), called the survival bandit problem (S-MAB). While in both problems, the objective is to maximize the s…
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs
Shinji Ito, Taira Tsuchiya, Junya Honda
This study considers online learning with general directed feedback graphs. For this problem, we present best-of-both-worlds algorithms that achieve nearly tight regret bounds for…