Search Results for author: Yuko Kuroki

Found 8 papers, 0 papers with code

Dynamic Structure Estimation from Bandit Feedback

no code implementations2 Jun 2022 Motoya Ohnishi, Isao Ishikawa, Yuko Kuroki, Masahiro Ikeda

This work present novel method for structure estimation of an underlying dynamical system.

Collaborative Pure Exploration in Kernel Bandit

no code implementations29 Oct 2021 Yihan Du, Wei Chen, Yuko Kuroki, Longbo Huang

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 limited communication and general reward functions, and is applicable to many online learning tasks, e. g., recommendation systems and network scheduling.

Decision Making online learning +1

Combinatorial Pure Exploration with Bottleneck Reward Function

no code implementations NeurIPS 2021 Yihan Du, Yuko Kuroki, Wei Chen

For the FC setting, we propose novel algorithms with optimal sample complexity for a broad family of instances and establish a matching lower bound to demonstrate the optimality (within a logarithmic factor).

Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback

no code implementations14 Jun 2020 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 $\mathcal{X} \subseteq \{0, 1\}^d$, and in each round the learner pulls an action $x \in \mathcal{X}$ and receives a random reward with expectation $x^{\top} \theta$, with $\theta \in \mathbb{R}^d$ a latent and unknown environment vector.

Graph Mining Meets Crowdsourcing: Extracting Experts for Answer Aggregation

no code implementations17 May 2019 Yasushi Kawase, Yuko Kuroki, Atsushi Miyauchi

Aggregating responses from crowd workers is a fundamental task in the process of crowdsourcing.

Graph Mining

Polynomial-time Algorithms for Multiple-arm Identification with Full-bandit Feedback

no code implementations27 Feb 2019 Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama

Based on our approximation algorithm, we propose novel bandit algorithms for the top-k selection problem, and prove that our algorithms run in polynomial time.

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