no code implementations • 2 Feb 2024 • Yuko Kuroki, Atsushi Miyauchi, Francesco Bonchi, Wei Chen
We study a general clustering setting in which we have $n$ elements to be clustered, and we aim to perform as few queries as possible to an oracle that returns a noisy sample of the similarity between two elements.
no code implementations • 24 Dec 2023 • Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi
We study best-of-both-worlds algorithms for $K$-armed linear contextual bandits.
no code implementations • 2 Jun 2022 • Motoya Ohnishi, Isao Ishikawa, Yuko Kuroki, Masahiro Ikeda
This work present novel method for structure estimation of an underlying dynamical system.
no code implementations • 29 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.
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).
no code implementations • 31 Dec 2020 • 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.
no code implementations • ICML 2020 • Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
Dense subgraph discovery aims to find a dense component in edge-weighted graphs.
no code implementations • 14 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.
no code implementations • 17 May 2019 • Yasushi Kawase, Yuko Kuroki, Atsushi Miyauchi
Aggregating responses from crowd workers is a fundamental task in the process of crowdsourcing.
no code implementations • 27 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.