| Do we need zero training loss after achieving zero training error? T Ishida, I Yamane, T Sakai, G Niu, M Sugiyama arXiv preprint arXiv:2002.08709, 2020 | 159 | 2020 |
| A one-step approach to covariate shift adaptation T Zhang, I Yamane, N Lu, M Sugiyama Asian Conference on Machine Learning, 65-80, 2020 | 25 | 2020 |
| Uplift modeling from separate labels I Yamane, F Yger, J Atif, M Sugiyama Advances in Neural Information Processing Systems 31, 2018 | 22 | 2018 |
| Multitask principal component analysis I Yamane, F Yger, M Berar, M Sugiyama Asian Conference on Machine Learning, 302-317, 2016 | 10 | 2016 |
| Is the performance of my deep network too good to be true? A direct approach to estimating the Bayes error in binary classification T Ishida, I Yamane, N Charoenphakdee, G Niu, M Sugiyama arXiv preprint arXiv:2202.00395, 2022 | 9 | 2022 |
| Regularized multitask learning for multidimensional log-density gradient estimation I Yamane, H Sasaki, M Sugiyama Neural Computation 28 (7), 1388-1410, 2016 | 7 | 2016 |
| Regularized multi-task learning for multi-dimensional log-density gradient estimation I Yamane, H Sasaki, M Sugiyama arXiv preprint arXiv:1508.00085, 2015 | 2 | 2015 |
| Scalable and hyper-parameter-free non-parametric covariate shift adaptation with conditional sampling F Portier, L Truquet, I Yamane arXiv preprint arXiv:2312.09969, 2023 | 1 | 2023 |
| Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality I Yamane, Y Chevaleyre, T Ishida, F Yger International Conference on Artificial Intelligence and Statistics, 4768-4801, 2023 | 1 | 2023 |
| Skew-symmetrically perturbed gradient flow for convex optimization F Futami, T Iwata, N Ueda, I Yamane Asian Conference on Machine Learning, 721-736, 2021 | 1 | 2021 |
| Mediated uncoupled learning: Learning functions without direct input-output correspondences I Yamane, J Honda, F Yger, M Sugiyama International Conference on Machine Learning, 11637-11647, 2021 | 1 | 2021 |
| A Fourier-Analytic Approach to List-Decoding for Sparse Random Linear Codes A Kawachi, I Yamane IEICE TRANSACTIONS on Information and Systems 98 (3), 532-540, 2015 | 1 | 2015 |
| Nearest Neighbor Sampling for Covariate Shift Adaptation F Portier, L Truquet, I Yamane | | 2024 |
| Regularized multi-task learning for multi-dimensional log-density gradient estimation I Yamane, H Sasaki, M Sugiyama IEICE Technical Report; IEICE Tech. Rep. 114 (306), 177-183, 2014 | | 2014 |
