70 citations · 245 across the 36 of their papers we have counts for
14 papers · 1 filter
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…
Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits
Junpei Komiyama, Edouard Fouché, Junya Honda
We consider nonstationary multi-armed bandit problems where the model parameters of the arms change over time. We introduce the adaptive resetting bandit (ADR-bandit), a bandit alg…
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
Ikko Yamane, Junya Honda, Florian Yger +1
Ordinary supervised learning is useful when we have paired training data of input and output . However, such paired data can be difficult to collect in practice. In this pap…
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring
Taira Tsuchiya, Junya Honda, Masashi Sugiyama
We investigate finite stochastic partial monitoring, which is a general model for sequential learning with limited feedback. While Thompson sampling is one of the most promising al…
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time
Hideaki Imamura, Nontawat Charoenphakdee, Futoshi Futami +3
The Gaussian process bandit is a problem in which we want to find a maximizer of a black-box function with the minimum number of function evaluations. If the black-box function var…
Efficient Adaptive Experimental Design for Average Treatment Effect Estimation
Masahiro Kato, Takuya Ishihara, Junya Honda +1
We study how to efficiently estimate average treatment effects (ATEs) using adaptive experiments. In adaptive experiments, experimenters sequentially assign treatments to experimen…
On the Calibration of Multiclass Classification with Rejection
Chenri Ni, Nontawat Charoenphakdee, Junya Honda +1
We investigate the problem of multiclass classification with rejection, where a classifier can choose not to make a prediction to avoid critical misclassification. First, we consid…
Dueling Bandits with Qualitative Feedback
Liyuan Xu, Junya Honda, Masashi Sugiyama
We formulate and study a novel multi-armed bandit problem called the qualitative dueling bandit (QDB) problem, where an agent observes not numeric but qualitative feedback by pulli…
Fully adaptive algorithm for pure exploration in linear bandits
Liyuan Xu, Junya Honda, Masashi Sugiyama
We propose the first fully-adaptive algorithm for pure exploration in linear bandits---the task to find the arm with the largest expected reward, which depends on an unknown parame…
Good Arm Identification via Bandit Feedback
Hideaki Kano, Junya Honda, Kentaro Sakamaki +3
We consider a novel stochastic multi-armed bandit problem called {\em good arm identification} (GAI), where a good arm is defined as an arm with expected reward greater than or equ…
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…