Kenshi Abe | DeepAI

AI Chat

Featured Co-authors

Alexandre Proutiere
35 publications
Masahiro Kato
28 publications
Junpei Komiyama
20 publications
Kaito Ariu
15 publications
Riku Togashi
14 publications
Masahiro Nomura
13 publications
Shota Yasui
11 publications
Atsushi Iwasaki
11 publications
Tetsuro Morimura
7 publications
Yuma Fujimoto
5 publications
Mitsuki Sakamoto
4 publications

∙ 07/13/2023

Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative

Dialog policies, which determine a system's action based on the current ...

0 Sho Shimoyama, et al. ∙

∙ 05/26/2023

A Slingshot Approach to Learning in Monotone Games

In this paper, we address the problem of computing equilibria in monoton...

0 Kenshi Abe, et al. ∙

∙ 05/23/2023

Memory Asymmetry Creates Heteroclinic Orbits to Nash Equilibrium in Learning in Zero-Sum Games

Learning in games considers how multiple agents maximize their own rewar...

0 Yuma Fujimoto, et al. ∙

∙ 05/02/2023

Exploration of Unranked Items in Safe Online Learning to Re-Rank

Bandit algorithms for online learning to rank (OLTR) problems often aim ...

0 Hiroaki Shiino, et al. ∙

∙ 02/02/2023

Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium

Repeated games consider a situation where multiple agents are motivated ...

0 Yuma Fujimoto, et al. ∙

∙ 09/09/2022

Fair Matrix Factorisation for Large-Scale Recommender Systems

Modern recommender systems are hedged with various requirements, such as...

0 Riku Togashi, et al. ∙

∙ 08/21/2022

Last-Iterate Convergence with Full- and Noisy-Information Feedback in Two-Player Zero-Sum Games

The theory of learning in games is prominent in the AI community, motiva...

4 Kenshi Abe, et al. ∙

∙ 06/18/2022

Mutation-Driven Follow the Regularized Leader for Last-Iterate Convergence in Zero-Sum Games

In this study, we consider a variant of the Follow the Regularized Leade...

9 Kenshi Abe, et al. ∙

∙ 06/02/2022

Policy Gradient Algorithms with Monte-Carlo Tree Search for Non-Markov Decision Processes

Policy gradient (PG) is a reinforcement learning (RL) approach that opti...

10 Tetsuro Morimura, et al. ∙

∙ 02/14/2022

Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search

This paper considers the capacity expansion problem in two-sided matchin...

10 Kenshi Abe, et al. ∙

∙ 10/23/2020

A Practical Guide of Off-Policy Evaluation for Bandit Problems

Off-policy evaluation (OPE) is the problem of estimating the value of a ...

13 Masahiro Kato, et al. ∙

∙ 10/22/2020

Thresholded LASSO Bandit

In this paper, we revisit sparse stochastic contextual linear bandits. I...

2 Kaito Ariu, et al. ∙

∙ 07/04/2020

Off-Policy Exploitability-Evaluation and Equilibrium-Learning in Two-Player Zero-Sum Markov Games

Off-policy evaluation (OPE) is the problem of evaluating new policies us...

0 Kenshi Abe, et al. ∙

∙ 11/18/2019

A Simple Heuristic for Bayesian Optimization with A Low Budget

The aim of black-box optimization is to optimize an objective function w...

0 Masahiro Nomura, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world