| Rethinking importance weighting for deep learning under distribution shift T Fang, N Lu, G Niu, M Sugiyama NeurIPS 2020 (spotlight), 2020 | 210 | 2020 |
| On the minimal supervision for training any binary classifier from only unlabeled data N Lu, G Niu, AK Menon, M Sugiyama ICLR 2019, 2018 | 117 | 2018 |
| Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach N Lu, T Zhang, G Niu, M Sugiyama AISTATS 2020, 2019 | 84 | 2019 |
| Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients N Lu, Z Wang, X Li, G Niu, Q Dou, M Sugiyama ICLR 2022, 2022 | 60 | 2022 |
| Machine learning from weak supervision: An empirical risk minimization approach M Sugiyama, H Bao, T Ishida, N Lu, T Sakai Adaptive Computation and Machine Learning series, The MIT Press, 2022 | 47 | 2022 |
| Pointwise Binary Classification with Pairwise Confidence Comparisons L Feng, S Shu, N Lu, B Han, M Xu, G Niu, B An, M Sugiyama ICML 2021, 2020 | 41 | 2020 |
| A one-step approach to covariate shift adaptation T Zhang, I Yamane, N Lu, M Sugiyama ACML 2020 (best paper), 2020 | 35 | 2020 |
| Binary classification from multiple unlabeled datasets via surrogate set classification N Lu, S Lei, G Niu, I Sato, M Sugiyama ICML 2021, 2021 | 21 | 2021 |
| Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems T Fang, N Lu, G Niu, M Sugiyama NeurIPS 2023 (spotlight), 2023 | 19 | 2023 |
| Rethinking importance weighting for transfer learning N Lu, T Zhang, T Fang, T Teshima, M Sugiyama Federated and Transfer Learning, 185-231, 2022 | 17 | 2022 |
| Multi-class classification from multiple unlabeled datasets with partial risk regularization Y Tang, N Lu, T Zhang, M Sugiyama ACML 2023, 2023 | 8* | 2023 |
| A general framework for learning under corruption: Label noise, attribute noise, and beyond L Iacovissi, N Lu, R Williamson | 4 | 2023 |
| Learning from Ambiguous Data with Hard Labels Z Xie, Z He, N Lu, L Bai, B Li, S Yang, M Sun, P Li ICASSP 2025, 2025 | | 2025 |
| Corruptions of Supervised Learning Problems: Typology and Mitigations L Iacovissi, N Lu, RC Williamson arXiv preprint arXiv:2307.08643, 2023 | | 2023 |
| A One-Step Approach to Covariate Shift Adaptation T Zhang, I Yamane, N Lu, M Sugiyama SN Computer Science 2 (4), 1-12, 2021 | | 2021 |
