17 citations · 43 across the 16 of their papers we have counts for
8 papers · 1 filter
Online Minimization of Polarization and Disagreement via Low-Rank Matrix Bandits
Federico Cinus, Yuko Kuroki, Atsushi Miyauchi +1
We study the problem of minimizing polarization and disagreement in the Friedkin-Johnsen opinion dynamics model under incomplete information. Unlike prior work that assumes a stati…
Multi-Play Combinatorial Semi-Bandit Problem
Shintaro Nakamura, Yuko Kuroki, Wei Chen
In the combinatorial semi-bandit (CSB) problem, a player selects an action from a combinatorial action set and observes feedback from the base arms included in the action. While CS…
Collaborative Pure Exploration in Kernel Bandit
Yihan Du, Wei Chen, Yuko Kuroki +1
In this paper, we formulate a Collaborative Pure Exploration in Kernel Bandit problem (CoPE-KB), which provides a novel model for multi-agent multi-task decision making under limit…
Combinatorial Pure Exploration with Bottleneck Reward Function
Yihan Du, Yuko Kuroki, Wei Chen
In this paper, we study the Combinatorial Pure Exploration problem with the Bottleneck reward function (CPE-B) under the fixed-confidence (FC) and fixed-budget (FB) settings. In CP…
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…
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback
Yihan Du, Yuko Kuroki, Wei Chen
In this paper, we first study the problem of combinatorial pure exploration with full-bandit feedback (CPE-BL), where a learner is given a combinatorial action space $\mathcal{X} \…
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…