70 citations · 159 across the 9 of their papers we have counts for
4 papers · 1 filter
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm
Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
We study the K-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms. The hard…
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring
Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
Partial monitoring is a general model for sequential learning with limited feedback formalized as a game between two players. In this game, the learner chooses an action and at the…
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem
Junpei Komiyama, Junya Honda, Hisashi Kashima +1
We study the -armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms. We int…
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays
Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
We discuss a multiple-play multi-armed bandit (MAB) problem in which several arms are selected at each round. Recently, Thompson sampling (TS), a randomized algorithm with a Bayesi…