Tomoya Sakai

According to our database, Tomoya Sakai authored at least 23 papers between 2014 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

A Generalized Backward Compatibility Metric.

Tomoya Sakai

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Information-Theoretic Representation Learning for Positive-Unlabeled Classification.

Tomoya Sakai, Gang Niu, Masashi Sugiyama

Neural Comput., 2021

Predictive Optimization with Zero-Shot Domain Adaptation.

Tomoya Sakai, Naoto Ohsaka

Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

Source Hypothesis Transfer for Zero-Shot Domain Adaptation.

Tomoya Sakai

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Causal Combinatorial Factorization Machines for Set-Wise Recommendation.

Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2021

Regret Minimization for Causal Inference on Large Treatment Space.

Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Robust modal regression with direct gradient approximation of modal regression risk.

Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

A Predictive Optimization Framework for Hierarchical Demand Matching.

Naoto Ohsaka, Tomoya Sakai, Akihiro Yabe

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Do We Need Zero Training Loss After Achieving Zero Training Error?

Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Robust modal regression with direct log-density derivative estimation.

Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori

CoRR, 2019

Covariate Shift Adaptation on Learning from Positive and Unlabeled Data.

Tomoya Sakai, Nobuyuki Shimizu

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Convex formulation of multiple instance learning from positive and unlabeled bags.

Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama

Neural Networks, 2018

Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning.

Tomoya Sakai, Gang Niu, Masashi Sugiyama

Mach. Learn., 2018

Semi-supervised AUC optimization based on positive-unlabeled learning.

Tomoya Sakai, Gang Niu, Masashi Sugiyama

Mach. Learn., 2018

Binary Matrix Completion Using Unobserved Entries.

Masayoshi Hayashi, Tomoya Sakai, Masashi Sugiyama

CoRR, 2018

2017

Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data.

Tomoya Sakai, Gang Niu, Masashi Sugiyama

CoRR, 2017

Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags.

Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama

CoRR, 2017

Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data.

Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama

Proceedings of the 34th International Conference on Machine Learning, 2017

Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds.

Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning.

Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama

CoRR, 2016

Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data.

Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Masashi Sugiyama

CoRR, 2016

Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning.

Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2014

Computationally Efficient Estimation of Squared-Loss Mutual Information with Multiplicative Kernel Models.

Tomoya Sakai, Masashi Sugiyama

IEICE Trans. Inf. Syst., 2014