Tomoya Sakai
Tomoya Sakai
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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
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-Volumeá…, 2017
Semi-supervised AUC optimization based on positive-unlabeled learning
T Sakai, G Niu, M Sugiyama
Machine Learning 107 (4), 767-794, 2018
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
Do We Need Zero Training Loss After Achieving Zero Training Error?
T Ishida, I Yamane, T Sakai, G Niu, M Sugiyama
arXiv preprint arXiv:2002.08709, 2020
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
Registration of infrared transmission images using squared-loss mutual information
T Sakai, M Sugiyama, K Kitagawa, K Suzuki
Precision Engineering 39, 187-193, 2015
Covariate shift adaptation on learning from positive and unlabeled data
T Sakai, N Shimizu
Proceedings of the AAAI Conference on Artificial Intelligence 33, 4838-4845, 2019
Least-squares log-density gradient clustering for Riemannian manifolds
M Ashizawa, H Sasaki, T Sakai, M Sugiyama
Artificial Intelligence and Statistics, 537-546, 2017
Binary matrix completion using unobserved entries
M Hayashi, T Sakai, M Sugiyama
arXiv preprint arXiv:1803.04663, 2018
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
Regret Minimization for Causal Inference on Large Treatment Space
A Tanimoto, T Sakai, T Takenouchi, H Kashima
arXiv preprint arXiv:2006.05616, 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
Information-Theoretic Representation Learning for Positive-Unlabeled Classification
T Sakai, G Niu, M Sugiyama
arXiv preprint arXiv:1710.05359, 2017
pywsl: python codes for weakly-supervised learning
T Sakai
GitHub, 2017
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