フォロー
Kenshi Abe
Kenshi Abe
CyberAgent, Inc.
確認したメール アドレス: cyberagent.co.jp - ホームページ
タイトル
引用先
引用先
Thresholded lasso bandit
K Ariu, K Abe, A Proutière
International Conference on Machine Learning (ICML 2022), 2022
232022
Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games
K Abe, Y Kaneko
International Conference on Autonomous Agents and Multiagent Systems (AAMAS …, 2021
16*2021
Mutation-Driven Follow the Regularized Leader for Last-Iterate Convergence in Zero-Sum Games
K Abe, M Sakamoto, A Iwasaki
Conference on Uncertainty in Artificial Intelligence (UAI 2022), 2022
132022
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games
K Abe, K Ariu, M Sakamoto, K Toyoshima, A Iwasaki
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2023
92023
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search
K Abe, J Komiyama, A Iwasaki
International Joint Conference on Artificial Intelligence (IJCAI 2022), 2022
92022
A practical guide of off-policy evaluation for bandit problems
M Kato, K Abe, K Ariu, S Yasui
arXiv preprint arXiv:2010.12470, 2020
32020
Online Learning for Bidding Agent in First Price Auction
G Morishita, K Abe, K Ogawa, Y Kaneko
AAAI-20 Workshop on Reinforcement Learning in Games, 2020
32020
Model-based minimum bayes risk decoding
Y Jinnai, T Morimura, U Honda, K Ariu, K Abe
arXiv preprint arXiv:2311.05263, 2023
22023
Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium
Y Fujimoto, K Ariu, K Abe
International Joint Conference on Artificial Intelligence (IJCAI 2023), 2023
22023
Policy Gradient Algorithms with Monte-Carlo Tree Search for Non-Markov Decision Processes
T Morimura, K Ota, K Abe, P Zhang
arXiv preprint arXiv:2206.01011, 2022
22022
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
R Togashi, K Abe, Y Saito
The Web Conference (WWW 2024), 2024
1*2024
クールノー競争におけるマルチエージェント強化学習に関する研究
豊島健太郎, 坂本充生, 阿部拳之, 岩崎敦
第 84 回全国大会講演論文集 2022 (1), 11-12, 2022
12022
Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization
M Kato, K Nakagawa, K Abe, T Morimura
arXiv preprint arXiv:2010.01404, 2020
1*2020
A simple heuristic for Bayesian optimization with a low budget
M Nomura, K Abe
arXiv preprint arXiv:1911.07790, 2019
12019
Learning Fair Division from Bandit Feedback
H Yamada, J Komiyama, K Abe, A Iwasaki
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2024
2024
Memory Asymmetry Creates Heteroclinic Orbits to Nash Equilibrium in Learning in Zero-Sum Games
Y Fujimoto, K Ariu, K Abe
Annual AAAI Conference on Artificial Intelligence (AAAI 2024), 2024
2024
Nash Equilibrium and Learning Dynamics in Three-Player Matching -Action Games
Y Fujimoto, K Ariu, K Abe
arXiv preprint arXiv:2402.10825, 2024
2024
Return-Aligned Decision Transformer
T Tanaka, K Abe, K Ariu, T Morimura, E Simo-Serra
arXiv preprint arXiv:2402.03923, 2024
2024
Slingshot Perturbation to Learning in Monotone Games
K Abe, K Ariu, M Sakamoto, A Iwasaki
2023
Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative
S Shimoyama, T Morimura, K Abe, T Takamichi, Y Tomomatsu, ...
arXiv preprint arXiv:2307.06721, 2023
2023
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