Computationally Efficient Estimation of Squared-Loss Mutual Information with Multiplicative Kernel Models. - Tomoya Sakai 001; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2014)

Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. - Gang Niu 001; Marthinus Christoffel du Plessis; Tomoya Sakai 001; Yao Ma; Masashi Sugiyama (NIPS, 2016)

Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data. - Gang Niu 001; Marthinus Christoffel du Plessis; Tomoya Sakai 001; Masashi Sugiyama (CoRR, 2016)

Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning. - Tomoya Sakai 001; Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (CoRR, 2016)

Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. - Mina Ashizawa; Hiroaki Sasaki; Tomoya Sakai 001; Masashi Sugiyama (AISTATS, 2017)

Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. - Tomoya Sakai 001; Marthinus Christoffel du Plessis; Gang Niu 001; Masashi Sugiyama (ICML, 2017)

Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. - Han Bao 002; Tomoya Sakai 001; Issei Sato; Masashi Sugiyama (CoRR, 2017)

Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (CoRR, 2017)

Binary Matrix Completion Using Unobserved Entries. - Masayoshi Hayashi; Tomoya Sakai 001; Masashi Sugiyama (CoRR, 2018)

Semi-supervised AUC optimization based on positive-unlabeled learning. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2018)

Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (Mach. Learn., 2018)

Convex formulation of multiple instance learning from positive and unlabeled bags. - Han Bao 002; Tomoya Sakai 001; Issei Sato; Masashi Sugiyama (Neural Networks, 2018)

Covariate Shift Adaptation on Learning from Positive and Unlabeled Data. - Tomoya Sakai 001; Nobuyuki Shimizu (AAAI, 2019)

Robust modal regression with direct log-density derivative estimation. - Hiroaki Sasaki; Tomoya Sakai 001; Takafumi Kanamori (CoRR, 2019)

Do We Need Zero Training Loss After Achieving Zero Training Error? - Takashi Ishida 001; Ikko Yamane; Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (ICML, 2020)

A Predictive Optimization Framework for Hierarchical Demand Matching. - Naoto Ohsaka; Tomoya Sakai 001; Akihiro Yabe (SDM, 2020)

Robust modal regression with direct gradient approximation of modal regression risk. - Hiroaki Sasaki; Tomoya Sakai 001; Takafumi Kanamori (UAI, 2020)

Regret Minimization for Causal Inference on Large Treatment Space. - Akira Tanimoto; Tomoya Sakai 001; Takashi Takenouchi; Hisashi Kashima (AISTATS, 2021)

Causal Combinatorial Factorization Machines for Set-Wise Recommendation. - Akira Tanimoto; Tomoya Sakai 001; Takashi Takenouchi; Hisashi Kashima (PAKDD (2), 2021)

Source Hypothesis Transfer for Zero-Shot Domain Adaptation. - Tomoya Sakai 001 (ECML/PKDD (1), 2021)

Predictive Optimization with Zero-Shot Domain Adaptation. - Tomoya Sakai 001; Naoto Ohsaka (SDM, 2021)

Information-Theoretic Representation Learning for Positive-Unlabeled Classification. - Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2021)

A Generalized Backward Compatibility Metric. - Tomoya Sakai 001 (KDD, 2022)