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2020 – today
- 2024
[j1]Tetsuro Morimura, Kazuhiro Ota, Kenshi Abe, Peinan Zhang:
Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes. RLJ 3: 1351-1376 (2024)- [c13]Yuma Fujimoto, Kaito Ariu, Kenshi Abe:
Memory Asymmetry Creates Heteroclinic Orbits to Nash Equilibrium in Learning in Zero-Sum Games. AAAI 2024: 17398-17406 - [c12]Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki:
Learning Fair Division from Bandit Feedback. AISTATS 2024: 3106-3114 - [c11]Tetsuro Morimura, Mitsuki Sakamoto, Yuu Jinnai, Kenshi Abe, Kaito Ariu:
Filtered Direct Preference Optimization. EMNLP 2024: 22729-22770 - [c10]Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Atsushi Iwasaki:
Adaptively Perturbed Mirror Descent for Learning in Games. ICML 2024 - [c9]Yuu Jinnai, Tetsuro Morimura, Ukyo Honda, Kaito Ariu, Kenshi Abe:
Model-Based Minimum Bayes Risk Decoding for Text Generation. ICML 2024 - [c8]Riku Togashi
, Kenshi Abe, Yuta Saito:
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems. WWW 2024: 3307-3318 - [i26]Tsunehiko Tanaka, Kenshi Abe, Kaito Ariu, Tetsuro Morimura, Edgar Simo-Serra:
Return-Aligned Decision Transformer. CoRR abs/2402.03923 (2024) - [i25]Yuma Fujimoto, Kaito Ariu, Kenshi Abe:
Nash Equilibrium and Learning Dynamics in Three-Player Matching m-Action Games. CoRR abs/2402.10825 (2024) - [i24]Riku Togashi, Kenshi Abe, Yuta Saito:
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems. CoRR abs/2402.14369 (2024) - [i23]Yuu Jinnai, Tetsuro Morimura, Kaito Ariu, Kenshi Abe:
Regularized Best-of-N Sampling to Mitigate Reward Hacking for Language Model Alignment. CoRR abs/2404.01054 (2024) - [i22]Tetsuro Morimura, Mitsuki Sakamoto, Yuu Jinnai, Kenshi Abe, Kaito Ariu:
Filtered Direct Preference Optimization. CoRR abs/2404.13846 (2024) - [i21]Yuma Fujimoto, Kaito Ariu, Kenshi Abe:
Global Behavior of Learning Dynamics in Zero-Sum Games with Memory Asymmetry. CoRR abs/2405.14546 (2024) - [i20]Yuma Fujimoto, Kaito Ariu, Kenshi Abe:
Synchronization behind Learning in Periodic Zero-Sum Games Triggers Divergence from Nash equilibrium. CoRR abs/2408.10595 (2024) - [i19]Kenshi Abe, Mitsuki Sakamoto, Kaito Ariu, Atsushi Iwasaki:
Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games. CoRR abs/2410.02388 (2024) - [i18]Noboru Isobe, Kenshi Abe, Kaito Ariu:
Last Iterate Convergence in Monotone Mean Field Games. CoRR abs/2410.05127 (2024) - [i17]Yuma Fujimoto, Kaito Ariu, Kenshi Abe:
Time-Varyingness in Auction Breaks Revenue Equivalence. CoRR abs/2410.12306 (2024) - 2023
- [c7]Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki:
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games. AISTATS 2023: 7999-8028 - [c6]Yuma Fujimoto, Kaito Ariu, Kenshi Abe:
Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium. IJCAI 2023: 118-125 - [c5]Hiroaki Shiino, Kaito Ariu, Kenshi Abe, Riku Togashi:
Exploration of Unranked Items in Safe Online Learning to Re-Rank. SIGIR 2023: 1991-1995 - [i16]Yuma Fujimoto, Kaito Ariu, Kenshi Abe:
Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium. CoRR abs/2302.01073 (2023) - [i15]Hiroaki Shiino, Kaito Ariu, Kenshi Abe, Riku Togashi:
Exploration of Unranked Items in Safe Online Learning to Re-Rank. CoRR abs/2305.01202 (2023) - [i14]Yuma Fujimoto, Kaito Ariu, Kenshi Abe:
Memory Asymmetry: A Key to Convergence in Zero-Sum Games. CoRR abs/2305.13619 (2023) - [i13]Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Atsushi Iwasaki:
A Slingshot Approach to Learning in Monotone Games. CoRR abs/2305.16610 (2023) - [i12]Sho Shimoyama, Tetsuro Morimura, Kenshi Abe, Toda Takamichi, Yuta Tomomatsu, Masakazu Sugiyama, Asahi Hentona, Yuuki Azuma, Hirotaka Ninomiya:
Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative. CoRR abs/2307.06721 (2023) - [i11]Yuu Jinnai, Tetsuro Morimura, Ukyo Honda, Kaito Ariu, Kenshi Abe:
Model-Based Minimum Bayes Risk Decoding. CoRR abs/2311.05263 (2023) - [i10]Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki:
Learning Fair Division from Bandit Feedback. CoRR abs/2311.09068 (2023) - 2022
- [c4]Kaito Ariu, Kenshi Abe, Alexandre Proutière:
Thresholded Lasso Bandit. ICML 2022: 878-928 - [c3]Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki:
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search. IJCAI 2022: 3-9 - [c2]Kenshi Abe, Mitsuki Sakamoto, Atsushi Iwasaki:
Mutation-driven follow the regularized leader for last-iterate convergence in zero-sum games. UAI 2022: 1-10 - [i9]Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki:
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search. CoRR abs/2202.06570 (2022) - [i8]Tetsuro Morimura, Kazuhiro Ota, Kenshi Abe, Peinan Zhang:
Policy Gradient Algorithms with Monte-Carlo Tree Search for Non-Markov Decision Processes. CoRR abs/2206.01011 (2022) - [i7]Kenshi Abe, Mitsuki Sakamoto, Atsushi Iwasaki:
Mutation-Driven Follow the Regularized Leader for Last-Iterate Convergence in Zero-Sum Games. CoRR abs/2206.09254 (2022) - [i6]Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki:
Last-Iterate Convergence with Full- and Noisy-Information Feedback in Two-Player Zero-Sum Games. CoRR abs/2208.09855 (2022) - [i5]Riku Togashi, Kenshi Abe:
Fair Matrix Factorisation for Large-Scale Recommender Systems. CoRR abs/2209.04394 (2022) - 2021
- [c1]Kenshi Abe, Yusuke Kaneko:
Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games. AAMAS 2021: 78-87 - 2020
- [i4]Kenshi Abe, Yusuke Kaneko:
Off-Policy Exploitability-Evaluation and Equilibrium-Learning in Two-Player Zero-Sum Markov Games. CoRR abs/2007.02141 (2020) - [i3]Kaito Ariu, Kenshi Abe, Alexandre Proutière:
Thresholded LASSO Bandit. CoRR abs/2010.11994 (2020) - [i2]Masahiro Kato, Kenshi Abe, Kaito Ariu, Shota Yasui:
A Practical Guide of Off-Policy Evaluation for Bandit Problems. CoRR abs/2010.12470 (2020)
2010 – 2019
- 2019
- [i1]Masahiro Nomura, Kenshi Abe:
A Simple Heuristic for Bayesian Optimization with A Low Budget. CoRR abs/1911.07790 (2019)
Coauthor Index
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