70 citations · 245 across the 36 of their papers we have counts for
38 papers
SAVER: Stochastic Adaptive Variance-Driven Exploration and Reconstruction for Low-Dose Computed Tomography
Shunta Nonaga, Koji Tabata, Junya Honda +3
Computed Tomography (CT) is indispensable in clinical diagnostics, yet minimizing radiation dose without compromising image quality remains a critical challenge. Conventional low-d…
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
Taira Tsuchiya, Shinji Ito, Junya Honda
Adaptivity to the difficulties of a problem is a key property in sequential decision-making problems to broaden the applicability of algorithms. Follow-the-regularized-leader (FTRL…
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms
Dorian Baudry, Kazuya Suzuki, Junya Honda
In this paper we propose a general methodology to derive regret bounds for randomized multi-armed bandit algorithms. It consists in checking a set of sufficient conditions on the s…
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits
Jongyeong Lee, Junya Honda, Chao-Kai Chiang +1
In the stochastic multi-armed bandit problem, a randomized probability matching policy called Thompson sampling (TS) has shown excellent performance in various reward models. In ad…
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…
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…
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation
Yuko Kuroki, Junya Honda, Masashi Sugiyama
Combinatorial optimization is one of the fundamental research fields that has been extensively studied in theoretical computer science and operations research. When developing an a…
Online Dense Subgraph Discovery via Blurred-Graph Feedback
Yuko Kuroki, Atsushi Miyauchi, Junya Honda +1
Dense subgraph discovery aims to find a dense component in edge-weighted graphs. This is a fundamental graph-mining task with a variety of applications and thus has received much a…
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…
Uncoupled Regression from Pairwise Comparison Data
Liyuan Xu, Junya Honda, Gang Niu +1
Uncoupled regression is the problem to learn a model from unlabeled data and the set of target values while the correspondence between them is unknown. Such a situation arises in p…
A Note on KL-UCB+ Policy for the Stochastic Bandit
Junya Honda
A classic setting of the stochastic K-armed bandit problem is considered in this note. In this problem it has been known that KL-UCB policy achieves the asymptotically optimal regr…
Polynomial-time Algorithms for Multiple-arm Identification with Full-bandit Feedback
Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi +2
We study the problem of stochastic combinatorial pure exploration (CPE), where an agent sequentially pulls a set of single arms (a.k.a. a super arm) and tries to find the best supe…
A Bad Arm Existence Checking Problem
Koji Tabata, Atsuyoshi Nakamura, Junya Honda +1
We study a bad arm existing checking problem in which a player's task is to judge whether a positive arm exists or not among given K arms by drawing as small number of arms as poss…
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…
Unsupervised Domain Adaptation Based on Source-guided Discrepancy
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao +3
Unsupervised domain adaptation is the problem setting where data generating distributions in the source and target domains are different, and labels in the target domain are unavai…
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…