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ORCIDTomoya Sakai 0001
Person information
- affiliation: NEC Research & Development Unit, Japan
Other persons with the same name
- Tomoya Sakai — disambiguation page
- Tomoya Sakai 0002 — Nagasaki University, Nagasaki, Japan (and 1 more)
- Tomoya Sakai 0003 — Fixstars Corporation, Tokyo, Japan (and 1 more)
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Journal Articles
- 2021
[j5]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. 33(1): 244-268 (2021)- 2018
- [j4]Tomoya Sakai, Gang Niu, Masashi Sugiyama
:
Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. 107(4): 767-794 (2018) - [j3]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. 107(4): 795 (2018) - [j2]Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama:
Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks 105: 132-141 (2018) - 2014
- [j1]Tomoya Sakai, Masashi Sugiyama:
Computationally Efficient Estimation of Squared-Loss Mutual Information with Multiplicative Kernel Models. IEICE Trans. Inf. Syst. 97-D(4): 968-971 (2014)
Conference and Workshop Papers
- 2022
- [c12]Tomoya Sakai:
A Generalized Backward Compatibility Metric. KDD 2022: 1525-1535 - 2021
- [c11]Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima:
Regret Minimization for Causal Inference on Large Treatment Space. AISTATS 2021: 946-954 - [c10]Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima:
Causal Combinatorial Factorization Machines for Set-Wise Recommendation. PAKDD (2) 2021: 498-509 - [c9]Tomoya Sakai:
Source Hypothesis Transfer for Zero-Shot Domain Adaptation. ECML/PKDD (1) 2021: 570-586 - [c8]Tomoya Sakai, Naoto Ohsaka:
Predictive Optimization with Zero-Shot Domain Adaptation. SDM 2021: 369-377 - 2020
- [c7]Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? ICML 2020: 4604-4614 - [c6]Naoto Ohsaka, Tomoya Sakai, Akihiro Yabe:
A Predictive Optimization Framework for Hierarchical Demand Matching. SDM 2020: 172-180 - [c5]Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori:
Robust modal regression with direct gradient approximation of modal regression risk. UAI 2020: 380-389 - 2019
- [c4]Tomoya Sakai, Nobuyuki Shimizu:
Covariate Shift Adaptation on Learning from Positive and Unlabeled Data. AAAI 2019: 4838-4845 - 2017
- [c3]Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama:
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. AISTATS 2017: 537-546 - [c2]Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. ICML 2017: 2998-3006 - 2016
- [c1]Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama:
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. NIPS 2016: 1199-1207
Informal and Other Publications
- 2021
- [i10]Tomoya Sakai, Naoto Ohsaka:
Predictive Optimization with Zero-Shot Domain Adaptation. CoRR abs/2101.06233 (2021) - 2020
- [i9]Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? CoRR abs/2002.08709 (2020) - [i8]Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima:
Regret Minimization for Causal Inference on Large Treatment Space. CoRR abs/2006.05616 (2020) - 2019
- [i7]Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori:
Robust modal regression with direct log-density derivative estimation. CoRR abs/1910.08280 (2019) - 2018
- [i6]Masayoshi Hayashi, Tomoya Sakai, Masashi Sugiyama:
Binary Matrix Completion Using Unobserved Entries. CoRR abs/1803.04663 (2018) - 2017
- [i5]Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama:
Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. CoRR abs/1704.06767 (2017) - [i4]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning. CoRR abs/1705.01708 (2017) - [i3]Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data. CoRR abs/1710.05359 (2017) - 2016
- [i2]Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Masashi Sugiyama:
Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data. CoRR abs/1603.03130 (2016) - [i1]Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama:
Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning. CoRR abs/1605.06955 (2016)
Coauthor Index
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