Clipped Matrix Completion: A Remedy for Ceiling Effects. - Takeshi Teshima; Miao Xu 001; Issei Sato; Masashi Sugiyama (AAAI, 2019)

Learning from Positive and Unlabeled Data with a Selection Bias. - Masahiro Kato; Takeshi Teshima; Junya Honda (ICLR (Poster), 2019)

Few-shot Domain Adaptation by Causal Mechanism Transfer. - Takeshi Teshima; Issei Sato; Masashi Sugiyama (ICML, 2020)

Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. - Takeshi Teshima; Isao Ishikawa; Koichi Tojo; Kenta Oono; Masahiro Ikeda; Masashi Sugiyama (NeurIPS, 2020)

γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator. - Masahiro Fujisawa; Takeshi Teshima; Issei Sato (CoRR, 2020)

Universal Approximation Property of Neural Ordinary Differential Equations. - Takeshi Teshima; Koichi Tojo; Masahiro Ikeda; Isao Ishikawa; Kenta Oono (CoRR, 2020)

γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. - Masahiro Fujisawa; Takeshi Teshima; Issei Sato; Masashi Sugiyama (AISTATS, 2021)

Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation. - Masahiro Kato; Takeshi Teshima (ICML, 2021)

Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. - Takeshi Teshima; Masashi Sugiyama (UAI, 2021)

Rethinking Importance Weighting for Transfer Learning. - Nan Lu; Tianyi Zhang; Tongtong Fang; Takeshi Teshima; Masashi Sugiyama (CoRR, 2021)

Universal Approximation Property of Invertible Neural Networks. - Isao Ishikawa; Takeshi Teshima; Koichi Tojo; Kenta Oono; Masahiro Ikeda; Masashi Sugiyama (J. Mach. Learn. Res., 2023)