坂井 智哉
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発表論文

論文誌

  1. T. Sakai, G. Niu, & M. Sugiyama
    Information-theoretic representation learning for positive-unlabeled classification.
    Neural Computation, vol.33, no.1, pp.244-268, 2021.
    [paper]

  2. H. Bao, T. Sakai, I. Sato, & M. Sugiyama
    Convex formulation of multiple instance learning from positive and unlabeled bags.
    Neural Networks, vol.105, pp.132-141, 2018.
    [paper] [preprint]

  3. T. Sakai, G. Niu, & M. Sugiyama
    Semi-supervised AUC optimization based on positive-unlabeled learning.
    Machine Learning, vol.107, no.4, pp.767-794, 2018.
    (Presented at Asian Conference on Machine Learning (ACML 2017), Seoul, Korea, 15-17, Nov., 2017)
    [paper] [preprint] [code (MATLAB)]

  4. T. Sakai, M. Sugiyama, K. Kitagawa, & K. Suzuki
    Registration of infrared transmission images using squared-loss mutual information.
    Precision Engineering, vol.39, pp.187-193, 2015.
    [paper]

  5. T. Sakai & M. Sugiyama
    Computationally efficient estimation of squared-loss mutual information with multiplicative kernel models.
    IEICE Transactions on Information and Systems, vol.E97-D, no.4, pp.968-971, 2014.
    [paper] [code (MATLAB)]

会議論文 （査読付き）

  1. T. Sakai
    A generalized backward compatibility metric.
    In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’22), pp. 1525–1535, 2022.
    (Presented at the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’22), Washington, DC, USA, August 14–18, 2022)
    (代理発表者：松野竜太さん)
    [paper]

  2. T. Sakai
    Source hypothesis transfer for zero-shot domain adaptation.
    In Machine Learning and Knowledge Discovery in Databases. Research Track, pp. 570–586, 2021.
    (Presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), Virtual, Sep. 13-17, 2021)
    [paper] [pdf]

  3. A. Tanimoto, T. Sakai, T. Takenouchi, & H. Kashima
    Causal combinatorial factorization machines for set-wise recommendation.
    In Advances in Knowledge Discovery and Data Mining, pp. 498–509, 2021.
    (Presented at the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2021), Virtual, May 11-14, 2021)
    [paper]

  4. A. Tanimoto, T. Sakai, T. Takenouchi, & H. Kashima
    Regret minimization for causal inference on large treatment space.
    In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), 2021.
    (Presented at the 24th International Conference on Artificial Intelligence and Statistics, Virtual, April 13-15, 2021)
    [paper]

  5. T. Sakai & N. Ohsaka
    Predictive optimization with zero-shot domain adaptation.
    In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM21), 2021.
    (Presented at the 2021 SIAM International Conference on Data Mining (SDM21), Virtual, April 29 - May 1, 2021)
    [paper] [preprint]

  6. T. Ishida, I. Yamane, T. Sakai, G. Niu, & M. Sugiyama
    Do we need zero training loss after achieving zero training error?
    In Proceedings of the Thirty-seventh International Conference on Machine Learning, PMLR, vol. 119, pp. 4604-4614, 2020.
    (Presented at Thirty-seventh International Conference on Machine Learning (ICML2020), Virtual, July 13-18, 2020)
    [paper] [preprint] [code (Python)]

  7. H. Sasaki, T. Sakai, & T. Kanamori
    Robust modal regression with direct log-density derivative estimation.
    In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence, PMLR, vol. 124, pp. 380-389, 2020.
    (Presented at Conference on Uncertainty in Artificial Intelligence (UAI2020), Virtual, Aug. 3-6, 2020)
    [paper] [preprint]

  8. N. Ohsaka, T. Sakai, & A. Yabe
    A predictive optimization framework for hierarchical demand matching.
    In Proceedings of the 2020 SIAM International Conference on Data Mining (SDM20), pp. 172-180, 2020.
    [paper]

  9. T. Sakai & N. Shimizu
    Covariate shift adaptation on learning from positive and unlabeled data.
    In Proceedings of the Thirty-third AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 4838-4845, 2019.
    (Presented at AAAI Conference on Artificial Intelligence (AAAI-19), Hawaii, USA, Jan. 27 - Feb 1, 2019)
    [paper]

  10. T. Sakai, M. C. du Plessis, G. Niu, & M. Sugiyama
    Semi-supervised classification based on classification from positive and unlabeled data.
    In D. Precup, Y.-W. Teh (Eds.), Proceedings of The 34th International Conference on Machine Learning (ICML 2017), vol. 70, pp. 2998-3006, 2017.
    (Presented at International Conference on Machine Learning (ICML 2017), Sydney, Australia, Aug. 6-11, 2017)
    [paper] [code (MATLAB)] [code (Python)]

  11. M. Ashizawa, H. Sasaki, T. Sakai, & M. Sugiyama
    Least-squares log-density gradient clustering for Riemannian manifolds.
    In A. Singh and J. Zhu (Eds.), Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54, pp. 537-546, 2017.
    (Presented at International Conference on Artificial Intelligence and Statistics (AISTATS 2017), Fort Lauderdale, USA, April 20-22, 2017)
    [paper] [code (MATLAB)]

  12. G. Niu, M. C. du Plessis, T. Sakai, Y. Ma, & M. Sugiyama
    Theoretical comparisons of positive-unlabeled learning against positive-negative learning.
    In D. D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 29, pp. 1199-1207, 2016.
    (Presented at Neural Information Processing Systems (NeurIPS2016), Barcelona, Spain, Dec. 5-8, 2016)
    [paper]

ワークショップ/技術報告/国内会議

  1. 佐久間 啓太, 坂井 智哉, 亀田 義男
    MLOpsを促進する予測ミス要因の自動特定法
    人工知能学会全国大会論文集 第35回全国大会, 2021.
    (2021年度 人工知能学会全国大会（第35回） (JSAI2021), オンライン, 6月8-11, 2021)
    [論文]

  2. 谷本 啓, 坂井 智哉, 竹之内 高志, 鹿島 久嗣
    多数の介入候補の下での意思決定のための因果効果評価
    人工知能学会全国大会論文集 第35回全国大会, 2021.
    (2021年度 人工知能学会全国大会（第35回） (JSAI2021), オンライン, 6月8-11, 2021)
    [論文]

  3. T. Ishida, I. Yamane, T. Sakai, G. Niu, & M. Sugiyama
    Do we need zero training loss after achieving zero training error?
    Presented at Virtual at The 23rd Information-Based Induction Sciences Workshop (IBIS2020), Virtual, Nov. 23-26, 2020.
    優秀発表賞ファイナリスト

  4. 谷本 啓, 坂井 智哉, 竹ノ内 高志, 鹿島 久嗣
    組合せ的行動空間におけるアウトカム予測
    人工知能学会全国大会論文集 第34回全国大会, 2020.
    (2020年度 人工知能学会全国大会（第34回） (JSAI2020), オンライン, 6月9-12, 2020)
    [論文]

  5. 坂井 智哉, 大坂 直人
    ゼロショットドメイン適応に基づいた予測最適化
    人工知能学会全国大会論文集 第34回全国大会, 2020.
    (2020年度 人工知能学会全国大会（第34回） (JSAI2020), オンライン, 6月9-12, 2020)
    [論文]

  6. 十河 泰弘, 坂井 智哉
    関連ドメインの予測モデルを活用した半教師付きドメイン適合.
    人工知能学会全国大会論文集 第33回全国大会, 2019.
    (2019年度 人工知能学会全国大会（第33回） (JSAI2019), 新潟, 6月4-7, 2019)
    [論文]

  7. 坂井 智哉, 十河 泰弘
    運用中の予測器を使って未知のクラスを分類する方法.
    人工知能学会全国大会論文集 第33回全国大会, 2019.
    (2019年度 人工知能学会全国大会（第33回） (JSAI2019), 新潟, 6月4-7, 2019)
    [論文]
    全国大会優秀賞（インタラクティブセッション発表部門）

  8. T. Sakai, G. Niu, & M. Sugiyama
    Semi-supervised AUC optimization based on positive-unlabeled learning.
    Presented at 情報系 Winter Festa Episode 3, Tokyo, Japan, Dec. 25-26, 2017.

  9. T. Sakai, G. Niu, & M. Sugiyama
    Semi-supervised AUC optimization based on positive-unlabeled learning.
    IEICE Technical Report, IBISML2017-40, vol. 117, no. 293, pp. 39-46, Nov., 2017.
    (Presented at The 20th Information-Based Induction Science Workshop (IBIS2017), Tokyo, Japan, Nov. 8-11, 2017)

  10. T. Sakai, M. C. du Plessis, G. Niu, & M. Sugiyama
    Semi-supervised classification based on positive-unlabeled classification.
    (Presented at First International Workshop on Symbolic-Neural Learning (SNL-2017), Nagoya, Japan, July 7-8, 2017.)

  11. H. Bao, T. Sakai, M. Sugiyama, & I. Sato
    Risk minimization framework for multiple instance learning from positive and unlabeled bags.
    (Presented at First International Workshop on Symbolic-Neural Learning (SNL-2017), Nagoya, Japan, July 7-8, 2017.)

  12. T. Sakai, M. C. du Plessis, G. Niu, & M. Sugiyama
    Semi-supervised classification based on positive-unlabeled classification.
    Presented at The Machine Learning Summer School (MLSS 2017 Tuebingen), Tuebingen, Germany, June 19-30, 2017.

  13. G. Niu (Presented by T. Sakai)
    Positive-unlabeled learning with application to semi-supervised learning.
    Presented at Microsoft Research Asia Academic Day 2017, Yilan, Taiwan, May 26, 2017.

  14. T. Sakai, M. C. du Plessis, G. Niu, & M. Sugiyama
    Semi-supervised classification based on classification from positive and unlabeled data.
    IEICE Technical Report, IBISML2016-80, pp.243-250, Kyoto, Japan, Nov. 16-19, 2016.
    (Presented at 2016 Workshop on Information-Based Induction Sciences (IBIS2016), Kyoto, Japan, Nov. 16-19, 2016)

  15. M. Ashizawa, H. Sasaki, T. Sakai, & M. Sugiyama
    Least-squares log-density gradient clustering for Riemannian manifolds.
    IEICE Technical Report, IBISML2015-96, pp.17-24, Tokyo, Japan, Mar. 17-18, 2016.

  16. T. Sakai & M. Sugiyama
    Estimation of squared-loss mutual information and its application in registration of infrared-transmission images.
    Presented at Machine Learning Summer School 2015 Kyoto (MLSS2015 Kyoto), no.27-11, Aug. 23-Sep. 4, 2015.

  17. T. Sakai & M. Sugiyama
    Image registration using squared-loss mutual information.
    Presented at 2014 Workshop on Information-Based Induction Sciences (IBIS2014), Nagoya, Japan, Nov. 16-19, 2014.

  18. T. Sakai, M. Sugiyama, K. Kitagawa, & K. Suzuki
    Registration of infrared-transmission images using squared-loss mutual information.
    In Proceedings of the Japan Society for Precision Engineering 2014 Spring Meeting, pp.973-974, Tokyo, Japan, Mar. 18-20, 2014.
    (Presented at the Japan Society for Precision Engineering 2014 Spring Meeting, Tokyo, Japan, Mar. 18-20, 2014)

  19. T. Sakai & M. Sugiyama
    Computationally efficient estimation of squared-loss mutual information with multiplicative kernel models.
    IEICE Technical Report, IBISML2013-53, pp.131-137, Tokyo, Japan, Nov. 10-13, 2013.
    (Presented at 2013 Workshop on Information-Based Induction Sciences (IBIS2013),Tokyo, Japan, Nov. 10-13, 2013)

著書

  1. M. Sugiyama, H. Bao, T. Ishida, N. Lu, T. Sakai, & G. Niu.
    Machine learning from weak supervision: An empirical risk minimization approach,
    MIT Press, Cambridge, MA, USA, 2022.
    [link] [Amazon] [Preview by Google Books]