土屋 平 / Taira Tsuchiya

English version is here.

News

• → 最新の News は主にこちら (English page) です．
• May 3, 2025: COLT 2025 に2本の論文が採択されました！ 1つ目の論文 "Corrupted Learning Dynamics in Games" は利得が未知のゲーム理論においてプレイヤーの行動選択や観測に汚染が入ったときの学習動力学の解析に関する論文（プレプリント） で， もう1つの論文は二人零和ゲームにおける Tsallis-INF アルゴリズムと問題依存リグレットに関する論文（プレプリント）です．
• Sep 26, 2024: NeurIPS 2024 に2本の論文が採択されました． 1つは minimax regret が $\Theta(T^{2/3})$ のオンライン学習に対する両環境最適方策についての論文（プレプリント，スライド） で， もう1つは確率的オンライン凸最適化で曲がった実行可能領域上で加速する方法についての論文（プレプリント，スライド）です．
• May 2, 2024: AISTATS 2024（論文）と ICML 2024（論文）に両環境最適バンディットアルゴリズムの論文が採択されました．
• Sep 22, 2023: 論文 "Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds," が NeurIPS 2023 に採択されました．プレプリントはこちら．
• Sep 6, 2023: FIT 2023 で最近の best-of-both-worlds 方策の進展についての（短時間の）招待講演をします． スライドはこちら．

論文

Preprints

1. Yuki Shibukawa, Taira Tsuchiya, Shinsaku Sakaue, and Kenji Yamanishi,
   "Bandit and Delayed Feedback in Online Structured Prediction,"
   arXiv:2502.18709, 2025.
   [arXiv]
   
2. Shinji Ito, Haipeng Luo, Taira Tsuchiya, and Yue Wu, (alphabetical order)
   "Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback,"
   arXiv:2502.17625, 2025. → accepted to COLT 2025
   [arXiv]
   
3. Shinsaku Sakaue, Taira Tsuchiya, Han Bao, and Taihei Oki,
   "Online Inverse Linear Optimization: Improved Regret Bound, Robustness to Suboptimality, and Toward Tight Regret Analysis,"
   arXiv:2501.14349, 2025.
   [arXiv]
   
4. Taira Tsuchiya, Shinji Ito, and Haipeng Luo,
   "Corrupted Learning Dynamics in Games,"
   arXiv:2412.07120, 2024. → accepted to COLT 2025
   [arXiv]
5. Kaito Ito and Taira Tsuchiya,
   "Online Control of Linear Systems with Unbounded and Degenerate Noise,"
   arXiv:2402.10252, 2024.
   [arXiv]
   

Conference Proceedings (peer-reviewed)

1. Shinsaku Sakaue, Han Bao, and Taira Tsuchiya,
   "Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret Analysis,"
   In Proceedings of 28th International Conference on Artificial Intelligence and Statistics (AISTATS 2025), pp.46–54, 2025.
   [paper] [arXiv]
   
2. Taira Tsuchiya and Shinji Ito,
   "A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds,"
   In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pp.8477–8514, 2024.
   [paper] [arXiv] [slide] [poster]
   
3. Taira Tsuchiya and Shinji Ito,
   "Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets,"
   In Advances in Neural Information Processing Systems 37 (NeurIPS 2024), pp.101671–101695, 2024.
   [paper] [arXiv] [slide] [poster]
   
4. Shinji Ito, Taira Tsuchiya, and Junya Honda,
   "Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds,"
   In Proceedings of Thirty Seventh Conference on Learning Theory (COLT 2024), pp.2522–2563, 2024.
   [paper] [arXiv]
   
5. Shinsaku Sakaue, Han Bao, Taira Tsuchiya, and Taihei Oki,
   "Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss,"
   In Proceedings of Thirty Seventh Conference on Learning Theory (COLT 2024), pp.4458–4486, 2024.
   [paper] [arXiv]
   
6. Taira Tsuchiya, Shinji Ito, and Junya Honda,
   "Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring,"
   In Proceedings of 41st International Conference on Machine Learning (ICML 2024), pp.48768–48790, 2024.
   [paper] [arXiv]
   
7. Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, and Nicolò Cesa-Bianchi,
   "Best-of-Both-Worlds Algorithms for Linear Contextual Bandits,"
   In Proceedings of 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), pp.1216–1224, 2024.
   [paper] [arXiv]
   
8. Taira Tsuchiya, Shinji Ito, and Junya Honda,
   "Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds,"
   In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), pp.47406–47437, 2023.
   [paper] [arXiv] [slide] [slide (short)] [poster]
   
9. Taira Tsuchiya, Shinji Ito, and Junya Honda,
   "Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits,"
   In Proceedings of 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), pp.8117–8144, 2023.
   [paper] [poster]
   
10. Junya Honda, Shinji Ito, and Taira Tsuchiya,
    "Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems,"
    In Proceedings of The 34th International Conference on Algorithmic Learning Theory (ALT 2023), pp.726–754, 2023.
    [paper]
    
11. Taira Tsuchiya, Shinji Ito, and Junya Honda,
    "Best-of-Both-Worlds Algorithms for Partial Monitoring,"
    In Proceedings of The 34th International Conference on Algorithmic Learning Theory (ALT 2023), pp.1484–1515, 2023.
    [paper] [arXiv] [slide]
    
12. Shinji Ito, Taira Tsuchiya, and Junya Honda,
    "Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs,"
    In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp.28631–28643, 2022.
    [paper] [arXiv]
    
13. Junpei Komiyama, Taira Tsuchiya, and Junya Honda,
    "Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification,"
    In Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp.10393–10404, 2022.
    [paper] [arXiv]
    
14. Shinji Ito, Taira Tsuchiya, and Junya Honda,
    "Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds,"
    In Proceedings of Thirty Fifth Conference on Learning Theory (COLT 2022), pp.1421–1422, 2022.
    [paper (extended abstract)] [full paper on arXiv] [slide]
    
15. Taira Tsuchiya, Junya Honda, and Masashi Sugiyama,
    "Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring,"
    In Advances in Neural Information Processing Systems 33 (NeurIPS 2020), pp.8861–8871, 2020.
    [paper] [full paper (pdf)] [arXiv] [poster]
    
16. Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, and Tetsunori Kobayashi,
    "Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning,"
    In Proceedings of 2018 IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP 2018), pp.2381–2385, 2018.
    [paper] [preprint]
    

Journal Papers (peer-reviewed)

1. Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, and Masashi Sugiyama,
   "Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization,"
   Neural Computation, 33(12):3361–3412, 2021.
   [arXiv] [paper]
   

Others (invited talks / domestic conferences / domestic proceedings / workshops)

1. 土屋 平，
   "両環境最適方策の進展,"
   Presented at オンライン予測数学ワークショップ2025, Feb. 23–24, 2025.
   
2. 伊藤 海斗，土屋 平，
   "非有界雑音下の線形システムに対するオンライン最適制御のリグレット解析,"
   Presented at 27th Information-Based Induction Sciences Workshop (IBIS 2024), Nov. 4–7, 2024.
   
3. Taira Tsuchiya，
   "Best of Both Worlds Algorithms in Online Decision Making Problems,"
   Presented at Machine Learning Summer School 2024, March 4–15, 2024.
   
4. 土屋 平，伊藤 伸志，本多 淳也，
   "オンライン意思決定問題における複数の観測量に同時に依存したリグレット上界を有する FTRL と，それを用いたスパース性依存上界やゲーム依存型上界，両環境最適性の実現,"
   Presented at 26th Information-Based Induction Sciences Workshop (IBIS 2023), Oct. 29– Nov. 1, 2023.
   学生優秀プレゼンテーション賞ファイナリスト [link]
   
5. 土屋 平，
   "組合せ半バンディット問題における適応的 best-of-both-worlds 方策"
   Presented at 第22回情報科学技術フォーラム（FIT2023）, 大阪公立大学　中百舌鳥キャンパス, Sept. 6–8, 2023.
   招待講演
   [slide] [slide (buildwise)]
6. 土屋 平，伊藤 伸志，本多 淳也，
   "Best-of-Both-Worlds Algorithms for Partial Monitoring,"
   Presented at 第125回人工知能基本問題研究会（SIG-FPAI）, vo.125, pp.47–53, Aug. 29–30, 2023.
   人工知能学会研究会優秀賞 [link]
7. 本多 淳也，伊藤 伸志，土屋 平，
   "バンディット問題における Follow-The-Perturbated-Leader 方策の確率的・ 敵対的最適性について"
   In Proceedings of Information-Based Induction Sciences and Machine Learning Workshop, vol.122, no.325, pp.30–37, Dec 2022.
   
8. 土屋 平，
   "部分観測問題における Best-of-Both-Worlds 方策"
   第3回数理・情報系研究集会, 東京工業大学, Dec. 15, 2022.
   
9. 土屋 平，伊藤 伸志，本多 淳也，
   "バンディット問題における Best-of-Both-Worlds 方策の進展：構造的バンディットと分散依存リグレット,"
   Presented at 25th Information-Based Induction Sciences Workshop (IBIS 2022), Nov. 20–23, 2022.
   学生最優秀プレゼンテーション賞 [link]
   
10. 小宮山純平，土屋 平，本多 淳也，
    "固定時間最適腕識別におけるミニマックス最適アルゴリズム,"
    Presented at 25th Information-Based Induction Sciences Workshop (IBIS 2022), Nov. 20–23, 2022.
    
11. Taira Tsuchiya，
    "Towards Practical Algorithms for Online Decision-Making,"
    Seminar at INRIA Lille Scool group, July 8, 2022.
    [slide]
12. Junta Fujinaga, Taira Tsuchiya, and Junya Honda,
    "組合せバンディット問題におけるInformation Directed SamplingのDC緩和について,"
    In Proceedings of Information-Based Induction Sciences and Machine Learning Workshop, vol.122, no.90, pp.129–136, June 2022.
    
13. 土屋 平，
    "部分観測問題におけるThompson抽出,"
    東芝研究開発センターシンポジウム2020 (online), Dec. 22, 2020.
    [slide]
    
14. 土屋 平，本多 淳也，杉山 将，
    "部分観測問題におけるトンプソン抽出アルゴリズムの設計とリグレット解析,"
    Presented at 23rd Information-Based Induction Sciences Workshop (IBIS 2020), Nov. 23–26, 2020.
    優秀発表賞 [link]
    [slide]
    
15. 土屋 平，Nontawat Charoenphakdee，佐藤 一誠，杉山 将，
    "経験リスク最小化による半教師付き順序回帰,"
    Presented at 22nd Information-Based Induction Sciences Workshop (IBIS 2019), Nov. 20–23, 2019.
    
16. Taira Tsuchiya, Tomoharu Iwata, and Tetsuji Ogawa,
    "Transformed Multiple Matrix Factorization: Towards Utilizing Heterogeneous Auxiliary Information,"
    In Proceedings of Information-Based Induction Sciences and Machine Learning Workshop, vol.117, no.475, pp.41–48, March 2018.
    
17. Naohiro Tawara, Taira Tsuchiya, Tetsuji Ogawa, and Tetsunori Kobayashi,
    "Speaker Feature Extraction using Adversarial Training,"
    In Proceedings of 2018 Spring Meeting of Acoustic Society of Japan (ASJ 2018), pp.141–144, March 2018.
    
18. Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, and Tetsunori Kobayashi,
    "Adversarial Multi-Task Learning for Extracting Speaker Invariant Feature for Zero-Resource Languages,"
    In Proceedings of 2018 Spring Meeting of Acoustic Society of Japan (ASJ 2018), pp.9–12, March 2018.
    

研究助成

1. 科研費 研究活動スタート支援，JSPS [link] (July 2024 – Mar. 2026)
2. ACT-X「数理・情報のフロンティア」加速フェーズ，JST [link] (Apr. 2024 – Mar. 2025)
3. ACT-X「数理・情報のフロンティア」，JST [link] (Oct. 2021 – Mar. 2024)
4. 日本学術振興会特別研究員 (DC1)，JSPS [link]
5. AIPチャレンジプログラム，JST (Aug. 2019 – Mar. 2020)

受賞など

1. 桜舞賞，理化学研究所，2025 [link]
2. （IBISML研究会賞，IBISML，2023）
3. 研究会優秀賞，人工知能学会，2023 [link]
4. 学生優秀プレゼンテーション賞ファイナリスト (Top 7 of approx. 100 student presentations)，IBIS2023，2023 [link]
5. 学生最優秀プレゼンテーション賞 (1st place out of approx. 100 student presentations)，IBIS2022，2022 [link]
6. 優秀発表賞 (Top 5 of 118 all presentations)，IBIS2020，2020 [link]
7. Travel Award, NeurIPS 2020
8. 東京大学トヨタ・ドワンゴ高度人工知能人材奨学金 (Apr. 2020 – Mar. 2021)
9. 東京大学トヨタ・ドワンゴ高度人工知能人材奨学金 (Apr. 2019 – Mar. 2020)
10. JEES・ソフトバンクAI人材育成奨学金 (Apr. 2019 – Mar. 2020)
11. 学科賞，早稲田大学， 2018

Work Experiences

1. Oct. 2023 – Present: Assistant Professor, The University of Tokyo
2. Apr. 2024 – Present: Visiting Scientist, RIKEN Center for Advanced Intelligence Project
3. Apr. 2021 – Sep. 2023: Research Fellowship for Young Scientists (DC1), JSPS
4. Feb. 2019 – Sep. 2023: RIKEN Center for Advanced Intelligence Project
   (Feb. 2019 – Dec. 2020, Apr. 2021 – Mar. 2022 research part-timer; Oct. 2020 – Dec. 2020 JRA; Apr. 2022 – Sep. 2023 trainee)

Education

1. Sep. 2023: Ph.D. in Department of Systems Science, Kyoto University (supervisor: Prof. Junya Honda)
   (with Department of Computer Science, The University of Tokyo for the first half of year)
2. Sep. 2020: M.S. in Department of Complexity Science, The University of Tokyo
3. Mar. 2018: B.E. in Department of Computer Science and Engineering, Waseda University

Teaching

1. Apr. 2025 – Present: 確率数理工学演習，東京大学工学部計数工学科
2. Apr. 2024 – Present: 学生実験「バーチャルネットベンチャー」，東京大学工学部計数工学科
3. Sep. 2023 – Present: オムニバス講義，東京大学工学部計数工学科
   (2023, 2024: 「オンライン凸最適化とバンディット問題」)
4. Sep. 2023 – Present: 短期演習「統計手法」，東京大学工学部計数工学科

Contact

• mail: tsuchiya [at] mist.i.u-tokyo.ac.jp