| Do we need zero training loss after achieving zero training error? T Ishida, I Yamane, T Sakai, G Niu, M Sugiyama Proceedings of the Thirty-seventh International Conference on Machine …, 2020 | 138 | 2020 |
| Theoretical comparisons of positive-unlabeled learning against positive-negative learning G Niu, MC du Plessis, T Sakai, Y Ma, M Sugiyama Advances in Neural Information Processing Systems 29, 1199-1207, 2016 | 137 | 2016 |
| Semi-supervised classification based on classification from positive and unlabeled data T Sakai, MC du Plessis, G Niu, M Sugiyama Proceedings of the 34th International Conference on Machine Learning, 2998-3006, 2017 | 133 | 2017 |
| Semi-supervised AUC optimization based on positive-unlabeled learning T Sakai, G Niu, M Sugiyama Machine Learning 107, 767-794, 2018 | 64 | 2018 |
| Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach M Sugiyama, H Bao, T Ishida, N Lu, T Sakai, G Niu MIT Press, 2022 | 22 | 2022 |
| Convex formulation of multiple instance learning from positive and unlabeled bags H Bao, T Sakai, I Sato, M Sugiyama Neural Networks 105, 132-141, 2018 | 20 | 2018 |
| Covariate shift adaptation on learning from positive and unlabeled data T Sakai, N Shimizu Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4838-4845, 2019 | 17 | 2019 |
| Computationally efficient estimation of squared-loss mutual information with multiplicative kernel models T Sakai, M Sugiyama IEICE TRANSACTIONS on Information and Systems 97 (4), 968-971, 2014 | 16 | 2014 |
| Regret minimization for causal inference on large treatment space A Tanimoto, T Sakai, T Takenouchi, H Kashima International Conference on Artificial Intelligence and Statistics, 946-954, 2021 | 11 | 2021 |
| Least-squares log-density gradient clustering for Riemannian manifolds M Ashizawa, H Sasaki, T Sakai, M Sugiyama Artificial Intelligence and Statistics, 537-546, 2017 | 9 | 2017 |
| Registration of infrared transmission images using squared-loss mutual information T Sakai, M Sugiyama, K Kitagawa, K Suzuki Precision Engineering 39, 187-193, 2015 | 7 | 2015 |
| Causal combinatorial factorization machines for set-wise recommendation A Tanimoto, T Sakai, T Takenouchi, H Kashima Pacific-Asia Conference on Knowledge Discovery and Data Mining, 498-509, 2021 | 3 | 2021 |
| Robust modal regression with direct gradient approximation of modal regression risk H Sasaki, T Sakai, T Kanamori Conference on Uncertainty in Artificial Intelligence, 380-389, 2020 | 3 | 2020 |
| A Generalized Backward Compatibility Metric T Sakai Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 2 | 2022 |
| MLOps を促進する予測ミス要因の自動特定法 佐久間啓太， 坂井智哉， 亀田義男 人工知能学会全国大会論文集 第 35 回 (2021), 2G3GS2e04-2G3GS2e04, 2021 | 2 | 2021 |
| Information-theoretic representation learning for positive-unlabeled classification T Sakai, G Niu, M Sugiyama Neural Computation 33 (1), 244-268, 2020 | 2 | 2020 |
| Binary matrix completion using unobserved entries M Hayashi, T Sakai, M Sugiyama arXiv preprint arXiv:1803.04663, 2018 | 2 | 2018 |
| Risk minimization framework for multiple instance learning from positive and unlabeled bags H Bao, T Sakai, I Sato, M Sugiyama arxiv preprint arxiv 1704, 2017 | 2 | 2017 |
| Distributionally robust model training V Barsopia, Y Kameda, T Sakai US Patent App. 17/392,261, 2022 | 1 | 2022 |
| Source hypothesis transfer for zero-shot domain adaptation T Sakai European Conference on Machine Learning and Principles and Practice of …, 2021 | 1 | 2021 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo在 k.u-tokyo.ac.jp 的电子邮件经过验证
