| 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 | 84 | 2017 |
| 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 | 83 | 2016 |
| Semi-supervised AUC optimization based on positive-unlabeled learning T Sakai, G Niu, M Sugiyama Machine Learning 107 (4), 767-794, 2018 | 38 | 2018 |
| 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 | 25 | 2020 |
| 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 | 14 | 2014 |
| 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 | 11* | 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 | 7 | 2019 |
| Registration of infrared transmission images using squared-loss mutual information T Sakai, M Sugiyama, K Kitagawa, K Suzuki Precision Engineering 39, 187-193, 2015 | 6 | 2015 |
| 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 | 3 | 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 | 3* | 2017 |
| Information-theoretic representation learning for positive-unlabeled classification T Sakai, G Niu, M Sugiyama Neural Computation 33 (1), 244-268, 2020 | 1 | 2020 |
| A predictive optimization framework for hierarchical demand matching N Ohsaka, T Sakai, A Yabe Proceedings of the 2020 SIAM International Conference on Data Mining, 172-180, 2020 | 1 | 2020 |
| Binary matrix completion using unobserved entries M Hayashi, T Sakai, M Sugiyama arXiv preprint arXiv:1803.04663, 2018 | 1 | 2018 |
| Predictive Optimization with Zero-Shot Domain Adaptation T Sakai, N Ohsaka Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | | 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 | | 2020 |
| pywsl: python codes for weakly-supervised learning T Sakai GitHub, 2017 | | 2017 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
