| Dataset shift in machine learning J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence Mit Press, 2008 | 3163 | 2008 |
| Co-teaching: Robust training of deep neural networks with extremely noisy labels B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, I Tsang, M Sugiyama Advances in neural information processing systems 31, 2018 | 2980 | 2018 |
| Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. M Sugiyama Journal of machine learning research 8 (5), 2007 | 1373 | 2007 |
| Covariate shift adaptation by importance weighted cross validation. M Sugiyama, M Krauledat, KR Müller Journal of Machine Learning Research 8 (5), 2007 | 1343 | 2007 |
| Direct importance estimation with model selection and its application to covariate shift adaptation M Sugiyama, S Nakajima, H Kashima, P Buenau, M Kawanabe Advances in neural information processing systems 20, 2007 | 1196 | 2007 |
| How does disagreement help generalization against label corruption? X Yu, B Han, J Yao, G Niu, I Tsang, M Sugiyama International conference on machine learning, 7164-7173, 2019 | 1105 | 2019 |
| Density ratio estimation in machine learning M Sugiyama, T Suzuki, T Kanamori Cambridge University Press, 2012 | 860 | 2012 |
| A least-squares approach to direct importance estimation T Kanamori, S Hido, M Sugiyama The Journal of Machine Learning Research 10, 1391-1445, 2009 | 714 | 2009 |
| Positive-unlabeled learning with non-negative risk estimator R Kiryo, G Niu, MC Du Plessis, M Sugiyama Advances in neural information processing systems 30, 2017 | 709 | 2017 |
| Change-point detection in time-series data by relative density-ratio estimation S Liu, M Yamada, N Collier, M Sugiyama Neural Networks 43, 72-83, 2013 | 688 | 2013 |
| Deep learning, reinforcement learning, and world models Y Matsuo, Y LeCun, M Sahani, D Precup, D Silver, M Sugiyama, E Uchibe, ... Neural Networks 152, 267-275, 2022 | 658 | 2022 |
| Learning discrete representations via information maximizing self-augmented training W Hu, T Miyato, S Tokui, E Matsumoto, M Sugiyama International conference on machine learning, 1558-1567, 2017 | 627 | 2017 |
| Machine learning in non-stationary environments: Introduction to covariate shift adaptation M Sugiyama, M Kawanabe MIT press, 2012 | 618 | 2012 |
| Direct importance estimation for covariate shift adaptation M Sugiyama, T Suzuki, S Nakajima, H Kashima, P Von Bünau, ... Annals of the Institute of Statistical Mathematics 60 (4), 699-746, 2008 | 585 | 2008 |
| Change-point detection in time-series data by direct density-ratio estimation Y Kawahara, M Sugiyama Proceedings of the 2009 SIAM international conference on data mining, 389-400, 2009 | 564 | 2009 |
| Attacks which do not kill training make adversarial learning stronger J Zhang, X Xu, B Han, G Niu, L Cui, M Sugiyama, M Kankanhalli International conference on machine learning, 11278-11287, 2020 | 561 | 2020 |
| Analysis of learning from positive and unlabeled data MC Du Plessis, G Niu, M Sugiyama Advances in neural information processing systems 27, 2014 | 535 | 2014 |
| Are anchor points really indispensable in label-noise learning? X Xia, T Liu, N Wang, B Han, C Gong, G Niu, M Sugiyama Advances in neural information processing systems 32, 2019 | 508 | 2019 |
| Local fisher discriminant analysis for supervised dimensionality reduction M Sugiyama Proceedings of the 23rd international conference on Machine learning, 905-912, 2006 | 494 | 2006 |
| Convex formulation for learning from positive and unlabeled data M Du Plessis, G Niu, M Sugiyama International conference on machine learning, 1386-1394, 2015 | 457 | 2015 |
Gang NiuSenior Research Scientist (tenured), RIKEN Center for Advanced Intelligence ProjectVerified email at riken.jp
