Takeshi Teshima

According to our database, Takeshi Teshima authored at least 11 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Universal Approximation Property of Invertible Neural Networks.

Isao Ishikawa, Takeshi Teshima, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama

J. Mach. Learn. Res., 2023

2021

Rethinking Importance Weighting for Transfer Learning.

Nan Lu, Tianyi Zhang, Tongtong Fang, Takeshi Teshima, Masashi Sugiyama

CoRR, 2021

Incorporating causal graphical prior knowledge into predictive modeling via simple data augmentation.

Takeshi Teshima, Masashi Sugiyama

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation.

Masahiro Kato, Takeshi Teshima

Proceedings of the 38th International Conference on Machine Learning, 2021

γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator.

Masahiro Fujisawa, Takeshi Teshima, Issei Sato, Masashi Sugiyama

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

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 Robust Divergence Estimator.

Masahiro Fujisawa, Takeshi Teshima, Issei Sato

CoRR, 2020

Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators.

Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Few-shot Domain Adaptation by Causal Mechanism Transfer.

Takeshi Teshima, Issei Sato, Masashi Sugiyama

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Learning from Positive and Unlabeled Data with a Selection Bias.

Masahiro Kato, Takeshi Teshima, Junya Honda

Proceedings of the 7th International Conference on Learning Representations, 2019

Clipped Matrix Completion: A Remedy for Ceiling Effects.

Takeshi Teshima, Miao Xu, Issei Sato, Masashi Sugiyama

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019