1 code implementation • 2 Jun 2015 • Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
Recently, Thompson sampling (TS), a randomized algorithm with a Bayesian spirit, has attracted much attention for its empirically excellent performance, and it is revealed to have an optimal regret bound in the standard single-play MAB problem.
1 code implementation • 8 Jun 2015 • Junpei Komiyama, Junya Honda, Hisashi Kashima, Hiroshi Nakagawa
We study the $K$-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms.
no code implementations • NeurIPS 2015 • Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
To show the optimality of PM-DMED with respect to the regret bound, we slightly modify the algorithm by introducing a hinge function (PM-DMED-Hinge).
no code implementations • 5 May 2016 • Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
We study the K-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms.
no code implementations • 13 Oct 2017 • Junpei Komiyama, Hajime Shimao
Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision.
no code implementations • NeurIPS 2017 • Junpei Komiyama, Junya Honda, Akiko Takeda
Motivated by online advertising, we study a multiple-play multi-armed bandit problem with position bias that involves several slots and the latter slots yield fewer rewards.
no code implementations • 13 Jun 2018 • Junpei Komiyama, Hajime Shimao
Fairness in algorithmic decision-making processes is attracting increasing concern.
1 code implementation • ICML 2018 • Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao
However, a fairness level as a constraint induces a nonconvexity of the feasible region, which disables the use of an off-the-shelf convex optimizer.
no code implementations • 11 Oct 2018 • Junpei Komiyama, Takanori Maehara
Statistical hypothesis testing serves as statistical evidence for scientific innovation.
no code implementations • 2 Oct 2019 • Kei Nakagawa, Masaya Abe, Junpei Komiyama
Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein.
1 code implementation • 2 Oct 2020 • Junpei Komiyama, Shunya Noda
Even a marginal imbalance in the population ratio frequently results in perpetual underestimation.
no code implementations • 15 Feb 2021 • Junpei Komiyama, Masaya Abe, Kei Nakagawa, Kenichiro McAlinn
We achieve superior statistical power to existing methods and prove that the false discovery rate is controlled.
1 code implementation • 23 Jul 2021 • Junpei Komiyama, Edouard Fouché, Junya Honda
We demonstrate that ADR-bandit has nearly optimal performance when abrupt or gradual changes occur in a coordinated manner that we call global changes.
no code implementations • 16 Sep 2021 • Kaito Ariu, Masahiro Kato, Junpei Komiyama, Kenichiro McAlinn, Chao Qin
We consider the "policy choice" problem -- otherwise known as best arm identification in the bandit literature -- proposed by Kasy and Sautmann (2021) for adaptive experimental design.
1 code implementation • 20 Sep 2021 • Junpei Komiyama, Shunya Noda
This paper proposes a new approach to training recommender systems called deviation-based learning.
1 code implementation • 18 Nov 2021 • Junpei Komiyama, Kaito Ariu, Masahiro Kato, Chao Qin
We consider best arm identification in the multi-armed bandit problem.
no code implementations • 10 Feb 2022 • Junpei Komiyama
The algorithm's performance is measured by the simple regret, that is, the quality of the estimated best arm.
1 code implementation • 14 Feb 2022 • Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki
Constructing a good search tree representation significantly boosts the performance of the proposed method.
1 code implementation • 9 Jun 2022 • Junpei Komiyama, Taira Tsuchiya, Junya Honda
We introduce two rates, $R^{\mathrm{go}}$ and $R^{\mathrm{go}}_{\infty}$, corresponding to lower bounds on the probability of misidentification, each of which is associated with a proposed algorithm.
no code implementations • 10 Jul 2022 • Katherine Hoffmann Pham, Junpei Komiyama
The sea crossing from Libya to Italy is one of the world's most dangerous and politically contentious migration routes, and yet over half a million people have attempted the crossing since 2014.
no code implementations • 15 Nov 2023 • Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki
This work addresses learning online fair division under uncertainty, where a central planner sequentially allocates items without precise knowledge of agents' values or utilities.
no code implementations • 12 Feb 2024 • Junpei Komiyama, Shinji Ito, Yuichi Yoshida, Souta Koshino
For the analysis of these algorithms, we propose a principled approach to limiting the probability of nonreplication.
no code implementations • 16 Feb 2024 • Kyoungseok Jang, Junpei Komiyama, Kazutoshi Yamazaki
This problem aims to find the arm of the largest mean with a fixed confidence level when the bandit model has been sampled from the known prior.