Tomoya Sakai

Publication Activity (10 Years)

Years Active: 2014-2021
Publications (10 Years): 25

Top Topics

Multiple Instance

Causal Inference

Top Venues

Publications

Tomoya Sakai, Naoto Ohsaka
Predictive Optimization with Zero-Shot Domain Adaptation. SDM (2021)
Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
Regret Minimization for Causal Inference on Large Treatment Space. AISTATS (2021)
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. 33 (1) (2021)
Tomoya Sakai
Source Hypothesis Transfer for Zero-Shot Domain Adaptation. ECML/PKDD (1) (2021)
Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
Causal Combinatorial Factorization Machines for Set-Wise Recommendation. PAKDD (2) (2021)
Tomoya Sakai, Naoto Ohsaka
Predictive Optimization with Zero-Shot Domain Adaptation. CoRR (2021)
Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
Do We Need Zero Training Loss After Achieving Zero Training Error? CoRR (2020)
Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
Robust modal regression with direct gradient approximation of modal regression risk. UAI (2020)
Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
Do We Need Zero Training Loss After Achieving Zero Training Error? ICML (2020)
Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
Regret Minimization for Causal Inference on Large Treatment Space. CoRR (2020)
Naoto Ohsaka, Tomoya Sakai, Akihiro Yabe
A Predictive Optimization Framework for Hierarchical Demand Matching. SDM (2020)
Tomoya Sakai, Nobuyuki Shimizu
Covariate Shift Adaptation on Learning from Positive and Unlabeled Data. AAAI (2019)
Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
Robust modal regression with direct log-density derivative estimation. CoRR (2019)
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. 107 (4) (2018)
Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks 105 (2018)
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. 107 (4) (2018)
Masayoshi Hayashi, Tomoya Sakai, Masashi Sugiyama
Binary Matrix Completion Using Unobserved Entries. CoRR (2018)
Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. AISTATS (2017)
Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. CoRR (2017)
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data. CoRR (2017)
Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. ICML (2017)
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning. CoRR (2017)
Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. NIPS (2016)
Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning. CoRR (2016)
Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Masashi Sugiyama
Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data. CoRR (2016)
Tomoya Sakai, Masashi Sugiyama
Computationally Efficient Estimation of Squared-Loss Mutual Information with Multiplicative Kernel Models. IEICE Trans. Inf. Syst. (4) (2014)