Kenshi Abe

According to our database, Kenshi Abe authored at least 22 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems.

Riku Togashi, Kenshi Abe, Yuta Saito

CoRR, 2024

Nash Equilibrium and Learning Dynamics in Three-Player Matching m-Action Games.

Yuma Fujimoto, Kaito Ariu, Kenshi Abe

CoRR, 2024

Return-Aligned Decision Transformer.

Tsunehiko Tanaka, Kenshi Abe, Kaito Ariu, Tetsuro Morimura, Edgar Simo-Serra

CoRR, 2024

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

Yuma Fujimoto, Kaito Ariu, Kenshi Abe

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Learning Fair Division from Bandit Feedback.

Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki

CoRR, 2023

Model-Based Minimum Bayes Risk Decoding.

Yuu Jinnai, Tetsuro Morimura, Ukyo Honda, Kaito Ariu, Kenshi Abe

CoRR, 2023

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

Sho Shimoyama, Tetsuro Morimura, Kenshi Abe, Toda Takamichi, Yuta Tomomatsu, Masakazu Sugiyama, Asahi Hentona, Yuuki Azuma, Hirotaka Ninomiya

CoRR, 2023

A Slingshot Approach to Learning in Monotone Games.

Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Atsushi Iwasaki

CoRR, 2023

Memory Asymmetry: A Key to Convergence in Zero-Sum Games.

Yuma Fujimoto, Kaito Ariu, Kenshi Abe

CoRR, 2023

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

Hiroaki Shiino, Kaito Ariu, Kenshi Abe, Riku Togashi

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

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

Yuma Fujimoto, Kaito Ariu, Kenshi Abe

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

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

Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Fair Matrix Factorisation for Large-Scale Recommender Systems.

Riku Togashi, Kenshi Abe

CoRR, 2022

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

Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki

CoRR, 2022

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

Tetsuro Morimura, Kazuhiro Ota, Kenshi Abe, Peinan Zhang

CoRR, 2022

Mutation-driven follow the regularized leader for last-iterate convergence in zero-sum games.

Kenshi Abe, Mitsuki Sakamoto, Atsushi Iwasaki

Proceedings of the Uncertainty in Artificial Intelligence, 2022

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

Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Thresholded Lasso Bandit.

Kaito Ariu, Kenshi Abe, Alexandre Proutière

Proceedings of the International Conference on Machine Learning, 2022

2021

Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games.

Kenshi Abe, Yusuke Kaneko

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020

A Practical Guide of Off-Policy Evaluation for Bandit Problems.

Masahiro Kato, Kenshi Abe, Kaito Ariu, Shota Yasui

CoRR, 2020

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

Kenshi Abe, Yusuke Kaneko

CoRR, 2020

2019

A Simple Heuristic for Bayesian Optimization with A Low Budget.

Masahiro Nomura, Kenshi Abe

CoRR, 2019