論文

ワーキングペーパー

1. Kiet Q. H. Vo, Siu Lun Chau, Masahiro Kato, Yixin Wang, Krikamol Muandet
   Strategic Learning with Local Explanations as Feedback
   [arXiv]（投稿中）

2. Masahiro Kato and Akari Ohda
   Asymptotically Unbiased Synthetic Control Methods by Density Matching
   [arXiv]（投稿中）

3. Masahiro Kato, Kyohei Okumura, Takuya Ishihara, and Toru Kitagawa
   Adaptive Experimental Design for Policy Learning
   [arXiv]

4. Masahiro Kato, Kei Nakagawa, Kenshi Abe,Tetsuro Morimura, and Kentaro Baba
   Direct Expected Quadratic Utility Maximization for Mean-Variance Controlled Reinforcement Learning
   First draft: 29 Sept 2020; Update 3 Apr 2021.
   [arXiv]

5. Masahiro Kato, Kota Matsui, and Ryo Inokuchi
   Double Debiased Covariate Shift Adaptation Robust to Density-Ratio Estimation
   [arXiv]（投稿中）

6. Masahiro Kato, Fumiaki Kozai, Ryo Inokuchi
   PUATE: Semiparametric Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
   [arXiv]（投稿中）

7. Masahiro Kato
   Adaptive Generalized Neyman Allocation: Local Asymptotic Minimax Optimal Best Arm Identification
   [arXiv]（投稿中）

8. Masahiro Kato and Kaito Ariu
   The Role of Contextual Information in Best Arm Identification
   First draft: 26 Jun 2021.
   [arXiv]（投稿中）

9. Kaito Ariu, Masahiro Kato, Jupei Komiyama, Kenichiro McAlinn, Chao Qin
   A Comment on "Adaptive Treatment Assignment in Experiments for Policy Choice" 2021
   Revise and Resubmit for Econometrica.

10. Masahiro Kato, Takuya Ishihara, Junya Honda, Yusuke Narita
    Efficient Adaptive Experimental Design for Average Treatment Effect Estimation
    First draft: 13 Feb 2020.
    [arXiv]（投稿中）

国際会議紀要

1. Masahiro Kato*, Shinji Ito
   LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
   In the International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
   https://openreview.net/forum?id=JFDRlIELhm 

2. Masahiro Kato*
   Analysis of the Temporal Structure in Economic Condition Assessments
   In IAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2024
   https://ieeexplore.ieee.org/abstract/document/10707884

3. Masahiro Kato*, Kentaro Baba, Hibiki Kaibuchi, Ryo Inokuchi
   Bayesian Portfolio Optimization by Predictive Synthesis
   In IAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2024
   https://ieeexplore.ieee.org/abstract/document/10708031/

4. Masahiro Kato*, Akihiro Oga, Wataru Komatsubara, and Ryo Inokuchi
   Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
   In the International Conference on Machine Learning (ICML, Oral 1.5%), 2024
   https://proceedings.mlr.press/v235/kato24a.html
   [スライド] [ポスター１]

5. Masahiro Kato*, Masaaki Imaizumi, and Kentaro Minami
   Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics
   In the International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
   https://arxiv.org/abs/2201.13127.

6. Shota Yasui*, and Masahiro Kato* (*Equal contribution)
   Learning Classifiers under Delayed Feedback with a Time Window Assumption
   In the International Conference on Knowledge Discovery and Data Mining (KDD), 2022
   https://dl.acm.org/doi/abs/10.1145/3534678.3539372.

7. Masahiro Kato*, Masaaiki Imaizumi, Kenichiro McAlinn, Shota Yasui, and Haruo Kakehi
   Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
   In the International Conference on Learning Representations (ICLR, Spotlight 4% 176/3391), 2021
   https://openreview.net/forum?id=7twQI5VnC8.

8. Masahiro Kato*, Kenichiro McAlinn, and Shota Yasui
   The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy
   In the Advances in Neural Information Processing Systems (NeurIPS), 2021
   https://openreview.net/forum?id=zyD5AiyLuzG.

9. Masahiro Kato*, and Takeshi Teshima
   Non-negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation. 
   In the International Conference on Machine Learning (ICML), 2021
   https://proceedings.mlr.press/v139/kato21a.

10. Riku Togashi, Masahiro Kato, Mayu Otani, Tetsuya Sakai, and Shin’ichi Satoh.
    Scalable Personalised Item Ranking through Parametric Density Estimation.
    In the Conference on Research and Development in Information Retrieval (SIGIR), 2021.
    https://dl.acm.org/doi/10.1145/3404835.3462933.

11. Riku Togashi, Masahiro Kato, Mayu Otani, and Shin’ichi Satoh
    Density-Ratio Based Personalised Ranking from Implicit Feedback.
    In the Web Conference (WWW), 2021.
    https://dl.acm.org/doi/10.1145/3442381.3450027.

12. Masatoshi Uehara*, Masahiro Kato*, and Shota Yasui (*Equal contribution)
    Off-Policy Evaluation and Learning for External Validity under a Covariate Shift.
    In the Advances in Neural Information Processing Systems (NeurIPS, Spotlight 3% 280/9054), 2020.
    https://proceedings.neurips.cc/paper/2020/hash/0084ae4bc24c0795d1e6a4f58444d39b-Abstract.html.

13. Masahiro Kato*, Takeshi Teshima, and Junya Honda
    Learning from Positive and Unlabeled Data with a Selection Bias. In the International Conference on Learning Representations (ICLR), 2019．
    https://openreview.net/forum?id=rJzLciCqKm.

国際学術誌

1. Junpei Komiyama, Kaito Ariu, Masahiro Kato, and Chao Qin
   Optimal simple regret in bayesian best arm identification
   Mathematics of Operations Research

2. Masahiro Kato, and Shinji Ito
   Best-of-Both-Worlds Linear Contextual Bandits
   Transactions on Machine Learning Research

国内会議紀要

1. Masahiro Kato
   Conformal Predictive Portfolio Selection
   JAFEE 2024 冬季大会
   [arXiv]

2. 椎名唯圭，加藤真大 ，井口亮
   独立成分分析と Fisher の線形判別による 内閣府景気ウォッチャー調査データの分析
   言語処理学会 第31回年次大会
   [paper]

3. 加藤真大, 浦川通, 田口雄哉, 新妻巧朗, 田森秀明, 羽根田賢和, 持橋大地
   線形判別分析のPU学習による朝日歌壇短歌の分析
   言語処理学会 第31回年次大会
   [paper]

4. 加藤真大, 浦川通, 田口雄哉, 新妻巧朗, 田森秀明, 羽根田賢和, 持橋大地
   文埋め込みに基づく朝日歌壇短歌の分析

5. 井口亮, 加藤真大, 貝淵響(みずほ第一フィナンシャルテクノロジー), 野田俊也, 今泉允聡(東京大学)
   密度比マッチングと勾配コミュニケーションによる異質性を伴う連合学習
   第32回 人工知能学会 金融情報学研究会（SIG-FIN）
   [paper]

6. 馬場健太郎, 加藤真大, 今井岳(みずほ第一フィナンシャルテクノロジー)
   二重PU学習による潜在的顧客の特定
   第32回 人工知能学会 金融情報学研究会（SIG-FIN）
   [paper]

7. 加藤真大, 貝淵響(みずほ第一フィナンシャルテクノロジー)
   ベイジアン予測統合に基づくポートフォリオ選択
   第32回 人工知能学会 金融情報学研究会（SIG-FIN）
   [paper]

ワークショップでの発表

1. Akira Fukuda, Masahiro Kato, Kenichiro McAlinn and Kosaku Takanashi,
   Bayesian Predictive Synthetic Control Methods
   In ICML 2023 Workshop on Counterfactuals in Minds and Machines.
   [Google Drive]

2. Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, and Toru Kitagawa
   Fixed-Budget Hypothesis Best Arm Identification: On the Information Loss in Experimental Design
   In ICML 2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems.
   [openreview]

3. Masahiro Kato
   Best Arm Identification with a Fixed Budget under a Small Gap
   Allied Social Sciences Association (ASSA) 2023 Annual Meeting
   [スライド]

4. Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, and Toru Kitagawa
   Semiparametric Best Arm Identification with Contextual Information
   In IBIS 2022.
   [arXiv] [poster]

5. 加藤真大
   経済学と機械学習：因果推論と密度比推定を中心に
   統計・機械学習若手シンポジウム 2022

[スライド]

6. Masahiro Kato
   Recent Findings on Density-Ratio Approaches in Machine Learning
   Workshop on Functional Inference and Machine Intelligence (FIMI) 2022
   [スライド]

7. Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, and Haruo Kakehi
   Learning Causal Relationships from Conditional Moment Conditions by Importance Weighting
   In NeurIPS 2021 Workshop on Machine Learning meets Econometrics.
   [arXiv]

8. Masashiro Kato, Kei Nakagawa, Kenchi Abe, and Tetsuro Morimura
   Direct Expected Quadratic Utility Maximization for Mean-Variance Controlled Reinforcement Learning
   In NeurIPS 2021 Workshop on Deep Reinforcement Learning.
   [arXiv]

9. 加藤真大
   効率的な因果推論と意思決定のための実験計画において異質性が果たす役割
   統計関連学会連合大会 2021
   [スライド]

10. Masahiro Kato, Shota Yasui, and Kenichiro McAlinn
    The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments.
    In ICML 2021 Workshop on The Neglected Assumptions In Causal Inference. [arXiv]

11. Masahiro Kato, Takuya Ishihara, Junya Honda, and Yusuke Narita
    Adaptive Experimental Design for Efficient Treatment Effect Estimation.
    In NeurIPS 2020 Workshop on Causal Discovery & Causality-Inspired Machine Learning.

12. 加藤真大
    平均処置効果の推定のための適応的実験計画
    慶應計量経済学ワークショップ  2020
    [スライド]

その他

1. Masahiro Kato, Kei Nakagawa, Kenshi Abe,Tetsuro Morimura, and Kentaro Baba
   Direct Expected Quadratic Utility Maximization for Mean-Variance Controlled Reinforcement Learning
   In the EEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)
   採択．ただし，紀要には掲載せず．
   [arXiv]

2. Masahiro Kato
   A Note on Doubly Robust Estimator in Regression Discontinuity Designs
   テクニカルノート
   [arXiv]
   
   

（*主著者・共同主著者）