17 citations · 43 across the 16 of their papers we have counts for
16 papers
A Survey on Algorithmic Interventions in Opinion Dynamics
Atsushi Miyauchi, Yuko Kuroki, Federico Cinus +2
Social media platforms have become critical infrastructures for public communication, where large-scale interaction can both support socially beneficial collective pressure and amp…
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…
Minimizing Polarization and Disagreement in the Friedkin-Johnsen Model with Unknown Innate Opinions
Federico Cinus, Atsushi Miyauchi, Yuko Kuroki +1
The bulk of the literature on opinion optimization in social networks adopts the Friedkin-Johnsen (FJ) opinion dynamics model, in which the innate opinions of all nodes are known:…
Query-Efficient Correlation Clustering with Noisy Oracle
Yuko Kuroki, Atsushi Miyauchi, Francesco Bonchi +1
We study a general clustering setting in which we have elements to be clustered, and we aim to perform as few queries as possible to an oracle that returns a noisy sample of th…
Dynamic Structure Estimation from Bandit Feedback using Nonvanishing Exponential Sums
Motoya Ohnishi, Isao Ishikawa, Yuko Kuroki +1
This work tackles the dynamic structure estimation problems for periodically behaved discrete dynamical system in the Euclidean space. We assume the observations become sequentiall…
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} \…
Non-zero-sum Stackelberg Budget Allocation Game for Computational Advertising
Daisuke Hatano, Yuko Kuroki, Yasushi Kawase +3
Computational advertising has been studied to design efficient marketing strategies that maximize the number of acquired customers. In an increased competitive market, however, a m…
Graph Mining Meets Crowdsourcing: Extracting Experts for Answer Aggregation
Yasushi Kawase, Yuko Kuroki, Atsushi Miyauchi
Aggregating responses from crowd workers is a fundamental task in the process of crowdsourcing. In cases where a few experts are overwhelmed by a large number of non-experts, most…
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 constant-ratio approximation algorithm for a class of hub-and-spoke network design problems and metric labeling problems: star metric case
Yuko Kuroki, Tomomi Matsui
Transportation networks frequently employ hub-and-spoke network architectures to route flows between many origin and destination pairs. Hub facilities work as switching points for…
Approximation Algorithm for Cycle-Star Hub Network Design Problems and Cycle-Metric Labeling Problems
Yuko Kuroki, Tomomi Matsui
We consider a single allocation hub-and-spoke network design problem which allocates each non-hub node to exactly one of given hub nodes so as to minimize the total transportation…