Takeshi Teshima
Cited by
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Learning from Positive and Unlabeled Data with a Selection Bias
M Kato, T Teshima, J Honda
Seventh International Conference on Learning Representations (ICLR 2019), 2019
Coupling-based invertible neural networks are universal diffeomorphism approximators
T Teshima*, I Ishikawa*, K Tojo, K Oono, M Ikeda, ...
Advances in Neural Information Processing Systems 33, 2020
Few-shot domain adaptation by causal mechanism transfer
T Teshima, I Sato, M Sugiyama
International Conference on Machine Learning, 9458-9469, 2020
Universal Approximation Property of Neural Ordinary Differential Equations
T Teshima, K Tojo, M Ikeda, I Ishikawa, K Oono
arXiv preprint arXiv:2012.02414, 2020
Non-negative bregman divergence minimization for deep direct density ratio estimation
M Kato, T Teshima
International Conference on Machine Learning, 5320-5333, 2021
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
M Fujisawa, T Teshima, I Sato, M Sugiyama
International Conference on Artificial Intelligence and Statistics, 1783-1791, 2021
Universal approximation property of invertible neural networks
I Ishikawa*, T Teshima*, K Tojo, K Oono, M Ikeda, ...
arXiv preprint arXiv:2204.07415, 2022
Clipped Matrix Completion: a Remedy for Ceiling Effects
T Teshima, M Xu, I Sato, M Sugiyama
Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 2019
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation
T Teshima, M Sugiyama
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial …, 2021
Rethinking Importance Weighting for Transfer Learning
N Lu, T Zhang, T Fang, T Teshima, M Sugiyama
Federated and Transfer Learning, 185-231, 2023
Forecasting Internally Displaced People’s Movements with Artificial Intelligence
A Oishi, T Teshima, K Akao, T Kano, M Kiriha, N Kojima, T Sasaki, ...
Digital Innovations, Business and Society in Africa, 311-339, 2022
Sampling unique molecular structures from autoencoders
T Teshima, H Kajino
US Patent App. 17/060,862, 2022
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