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2020 – today
- 2023
[j1]Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Universal Approximation Property of Invertible Neural Networks. J. Mach. Learn. Res. 24: 287:1-287:68 (2023)- 2022
- [i9]Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Universal approximation property of invertible neural networks. CoRR abs/2204.07415 (2022) - 2021
- [c7]Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama:
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator. AISTATS 2021: 1783-1791 - [c6]Masahiro Kato, Takeshi Teshima:
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation. ICML 2021: 5320-5333 - [c5]Takeshi Teshima, Masashi Sugiyama:
Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation. UAI 2021: 86-96 - [i8]Takeshi Teshima, Masashi Sugiyama:
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation. CoRR abs/2103.00136 (2021) - [i7]Nan Lu, Tianyi Zhang, Tongtong Fang, Takeshi Teshima, Masashi Sugiyama:
Rethinking Importance Weighting for Transfer Learning. CoRR abs/2112.10157 (2021) - 2020
- [c4]Takeshi Teshima, Issei Sato, Masashi Sugiyama:
Few-shot Domain Adaptation by Causal Mechanism Transfer. ICML 2020: 9458-9469 - [c3]Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. NeurIPS 2020 - [i6]Takeshi Teshima, Issei Sato, Masashi Sugiyama:
Few-shot Domain Adaptation by Causal Mechanism Transfer. CoRR abs/2002.03497 (2020) - [i5]Masahiro Kato, Takeshi Teshima:
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation. CoRR abs/2006.06979 (2020) - [i4]Masahiro Fujisawa, Takeshi Teshima, Issei Sato:
γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator. CoRR abs/2006.07571 (2020) - [i3]Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama:
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators. CoRR abs/2006.11469 (2020) - [i2]Takeshi Teshima, Koichi Tojo, Masahiro Ikeda, Isao Ishikawa, Kenta Oono:
Universal Approximation Property of Neural Ordinary Differential Equations. CoRR abs/2012.02414 (2020)
2010 – 2019
- 2019
- [c2]Takeshi Teshima, Miao Xu
, Issei Sato, Masashi Sugiyama:
Clipped Matrix Completion: A Remedy for Ceiling Effects. AAAI 2019: 5151-5158 - [c1]Masahiro Kato, Takeshi Teshima, Junya Honda:
Learning from Positive and Unlabeled Data with a Selection Bias. ICLR (Poster) 2019 - 2018
- [i1]Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama:
Clipped Matrix Completion: a Remedy for Ceiling Effects. CoRR abs/1809.04997 (2018)
Coauthor Index
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