| 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 | 127 | 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 | 115 | 2020 |
| Few-shot domain adaptation by causal mechanism transfer T Teshima, I Sato, M Sugiyama International Conference on Machine Learning, 9458-9469, 2020 | 94 | 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 | 44 | 2020 |
| Non-negative bregman divergence minimization for deep direct density ratio estimation M Kato, T Teshima International Conference on Machine Learning, 5320-5333, 2021 | 30 | 2021 |
| Universal approximation property of invertible neural networks I Ishikawa*, T Teshima*, K Tojo, K Oono, M Ikeda, ... Journal of Machine Learning Research 24 (287), 1−68, 2023 | 27 | 2023 |
| 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 c, 2021 | 15 | 2021 |
| Á-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 | 11 | 2021 |
| Rethinking importance weighting for transfer learning N Lu, T Zhang, T Fang, T Teshima, M Sugiyama Federated and Transfer Learning, 185-231, 2022 | 9 | 2022 |
| Forecasting Internally Displaced Peoplefs 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: New Frontiers and a c, 2022 | 5 | 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 | 5 | 2019 |
| Sampling unique molecular structures from autoencoders T Teshima, H Kajino US Patent App. 17/060,862, 2022 | | 2022 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
