Tomoya Sakai - RankMe

27 papers A* 4A 5Journal 15Unranked 3

Year	Rank	Type	Title / Venue / Authors
2022	A*	`conf`	A Generalized Backward Compatibility Metric. KDD Tomoya Sakai
2021	—	`conf`	Causal Combinatorial Factorization Machines for Set-Wise Recommendation. PAKDD (2) Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
2021	J	`jnl`	Information-Theoretic Representation Learning for Positive-Unlabeled Classification. Neural Comput. Tomoya Sakai, Gang Niu, Masashi Sugiyama
2021	A	`conf`	Predictive Optimization with Zero-Shot Domain Adaptation. SDM Tomoya Sakai, Naoto Ohsaka
2021	J	`jnl`	Predictive Optimization with Zero-Shot Domain Adaptation. CoRR Tomoya Sakai, Naoto Ohsaka
2021	A	`conf`	Regret Minimization for Causal Inference on Large Treatment Space. AISTATS Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
2021	—	`conf`	Source Hypothesis Transfer for Zero-Shot Domain Adaptation. ECML/PKDD (1) Tomoya Sakai
2020	A	`conf`	A Predictive Optimization Framework for Hierarchical Demand Matching. SDM Naoto Ohsaka, Tomoya Sakai, Akihiro Yabe
2020	A*	`conf`	Do We Need Zero Training Loss After Achieving Zero Training Error? ICML Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Do We Need Zero Training Loss After Achieving Zero Training Error? CoRR Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Regret Minimization for Causal Inference on Large Treatment Space. CoRR Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
2020	A	`conf`	Robust modal regression with direct gradient approximation of modal regression risk. UAI Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
2019	A*	`conf`	Covariate Shift Adaptation on Learning from Positive and Unlabeled Data. AAAI Tomoya Sakai, Nobuyuki Shimizu
2019	J	`jnl`	Robust modal regression with direct log-density derivative estimation. CoRR Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
2018	J	`jnl`	Binary Matrix Completion Using Unobserved Entries. CoRR Masayoshi Hayashi, Tomoya Sakai, Masashi Sugiyama
2018	J	`jnl`	Convex formulation of multiple instance learning from positive and unlabeled bags. Neural Networks Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
2018	J	`jnl`	Correction to: Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. Tomoya Sakai, Gang Niu, Masashi Sugiyama
2018	J	`jnl`	Semi-supervised AUC optimization based on positive-unlabeled learning. Mach. Learn. Tomoya Sakai, Gang Niu, Masashi Sugiyama
2017	J	`jnl`	Estimation of Squared-Loss Mutual Information from Positive and Unlabeled Data. CoRR Tomoya Sakai, Gang Niu, Masashi Sugiyama
2017	A	`conf`	Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds. AISTATS Mina Ashizawa, Hiroaki Sasaki, Tomoya Sakai, Masashi Sugiyama
2017	J	`jnl`	Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags. CoRR Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama
2017	J	`jnl`	Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning. CoRR Tomoya Sakai, Gang Niu, Masashi Sugiyama
2017	A*	`conf`	Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. ICML Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2016	J	`jnl`	Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning. CoRR Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu, Masashi Sugiyama
2016	J	`jnl`	Theoretical Comparisons of Learning from Positive-Negative, Positive-Unlabeled, and Negative-Unlabeled Data. CoRR Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Masashi Sugiyama
2016	—	`conf`	Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning. NIPS Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai, Yao Ma, Masashi Sugiyama
2014	J	`jnl`	Computationally Efficient Estimation of Squared-Loss Mutual Information with Multiplicative Kernel Models. IEICE Trans. Inf. Syst. Tomoya Sakai, Masashi Sugiyama