A Simple Heuristic for Bayesian Optimization with A Low Budget. - Masahiro Nomura; Kenshi Abe (CoRR, 2019)

Off-Policy Exploitability-Evaluation and Equilibrium-Learning in Two-Player Zero-Sum Markov Games. - Kenshi Abe; Yusuke Kaneko (CoRR, 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 in Two-Player Zero-Sum Markov Games. - Kenshi Abe; Yusuke Kaneko (AAMAS, 2021)

Thresholded Lasso Bandit. - Kaito Ariu; Kenshi Abe; Alexandre Proutière (ICML, 2022)

Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search. - Kenshi Abe; Junpei Komiyama; Atsushi Iwasaki (IJCAI, 2022)

Mutation-driven follow the regularized leader for last-iterate convergence in zero-sum games. - Kenshi Abe; Mitsuki Sakamoto; Atsushi Iwasaki (UAI, 2022)

Policy Gradient Algorithms with Monte-Carlo Tree Search for Non-Markov Decision Processes. - Tetsuro Morimura; Kazuhiro Ota; Kenshi Abe; Peinan Zhang (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)

Fair Matrix Factorisation for Large-Scale Recommender Systems. - Riku Togashi; Kenshi Abe (CoRR, 2022)

Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games. - Kenshi Abe; Kaito Ariu; Mitsuki Sakamoto; Kentaro Toyoshima; Atsushi Iwasaki (AISTATS, 2023)

Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium. - Yuma Fujimoto; Kaito Ariu; Kenshi Abe (IJCAI, 2023)

Exploration of Unranked Items in Safe Online Learning to Re-Rank. - Hiroaki Shiino; Kaito Ariu; Kenshi Abe; Riku Togashi (SIGIR, 2023)

Memory Asymmetry: A Key to Convergence in Zero-Sum Games. - Yuma Fujimoto; Kaito Ariu; Kenshi Abe (CoRR, 2023)

A Slingshot Approach to Learning in Monotone Games. - Kenshi Abe; Kaito Ariu; Mitsuki Sakamoto; Atsushi Iwasaki (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)

Model-Based Minimum Bayes Risk Decoding. - Yuu Jinnai; Tetsuro Morimura; Ukyo Honda; Kaito Ariu; Kenshi Abe (CoRR, 2023)

Memory Asymmetry Creates Heteroclinic Orbits to Nash Equilibrium in Learning in Zero-Sum Games. - Yuma Fujimoto; Kaito Ariu; Kenshi Abe (AAAI, 2024)

Learning Fair Division from Bandit Feedback. - Hakuei Yamada; Junpei Komiyama; Kenshi Abe; Atsushi Iwasaki (AISTATS, 2024)

Filtered Direct Preference Optimization. - Tetsuro Morimura; Mitsuki Sakamoto; Yuu Jinnai; Kenshi Abe; Kaito Ariu (EMNLP, 2024)

Adaptively Perturbed Mirror Descent for Learning in Games. - Kenshi Abe; Kaito Ariu; Mitsuki Sakamoto; Atsushi Iwasaki (ICML, 2024)

Model-Based Minimum Bayes Risk Decoding for Text Generation. - Yuu Jinnai; Tetsuro Morimura; Ukyo Honda; Kaito Ariu; Kenshi Abe (ICML, 2024)

Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes. - Tetsuro Morimura; Kazuhiro Ota; Kenshi Abe; Peinan Zhang (RLJ, 2024)

Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems. - Riku Togashi; Kenshi Abe; Yuta Saito (WWW, 2024)

Nash Equilibrium and Learning Dynamics in Three-Player Matching m-Action Games. - Yuma Fujimoto; Kaito Ariu; Kenshi Abe (CoRR, 2024)

Regularized Best-of-N Sampling to Mitigate Reward Hacking for Language Model Alignment. - Yuu Jinnai; Tetsuro Morimura; Kaito Ariu; Kenshi Abe (CoRR, 2024)

Synchronization behind Learning in Periodic Zero-Sum Games Triggers Divergence from Nash equilibrium. - Yuma Fujimoto; Kaito Ariu; Kenshi Abe (CoRR, 2024)

Last Iterate Convergence in Monotone Mean Field Games. - Noboru Isobe; Kenshi Abe; Kaito Ariu (CoRR, 2024)

Time-Varyingness in Auction Breaks Revenue Equivalence. - Yuma Fujimoto; Kaito Ariu; Kenshi Abe (CoRR, 2024)

Synchronization in Learning in Periodic Zero-Sum Games Triggers Divergence from Nash Equilibrium. - Yuma Fujimoto; Kaito Ariu; Kenshi Abe (AAAI, 2025)

Approximate State Abstraction for Markov Games. - Hiroki Ishibashi; Kenshi Abe; Atsushi Iwasaki (AAAI, 2025)

Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games. - Kenshi Abe; Mitsuki Sakamoto; Kaito Ariu; Atsushi Iwasaki (ICLR, 2025)

Global Behavior of Learning Dynamics in Zero-Sum Games with Memory Asymmetry. - Yuma Fujimoto; Kaito Ariu; Kenshi Abe (AAMAS, 2025)

Nash Equilibrium and Learning Dynamics in Three-Player Matching m-Action Games. - Yuma Fujimoto; Kaito Ariu; Kenshi Abe (AAMAS, 2025)

Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment. - Yuu Jinnai; Tetsuro Morimura; Kaito Ariu; Kenshi Abe (NAACL (Long Papers), 2025)

Efficient Creative Selection in Online Advertising using Top-Two Thompson Sampling. - Daiki Katsuragawa; Yusuke Kaneko; Kaito Ariu; Kenshi Abe (WSDM, 2025)

Evaluation of Best-of-N Sampling Strategies for Language Model Alignment. - Yuki Ichihara; Yuu Jinnai; Tetsuro Morimura; Kenshi Abe; Kaito Ariu; Mitsuki Sakamoto; Eiji Uchibe (Trans. Mach. Learn. Res., 2025)

Return-Aligned Decision Transformer. - Tsunehiko Tanaka; Kenshi Abe; Kaito Ariu; Tetsuro Morimura; Edgar Simo-Serra (Trans. Mach. Learn. Res., 2025)

The Power of Perturbation under Sampling in Solving Extensive-Form Games. - Wataru Masaka; Mitsuki Sakamoto; Kenshi Abe; Kaito Ariu; Tuomas Sandholm; Atsushi Iwasaki (CoRR, 2025)

Policy Testing in Markov Decision Processes. - Kaito Ariu; Po-An Wang; Alexandre Proutière; Kenshi Abe (CoRR, 2025)