| 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 |
| 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 |
| Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78 (1), 35-61, 2010 | 381 | 2010 |
| Higher-order explanations of graph neural networks via relevant walks T Schnake, O Eberle, J Lederer, S Nakajima, KT Schütt, KR Müller, ... IEEE transactions on pattern analysis and machine intelligence 44 (11), 7581 …, 2021 | 374 | 2021 |
| Multi-class image segmentation using conditional random fields and global classification N Plath, M Toussaint, S Nakajima Proceedings of the 26th annual international conference on machine learning …, 2009 | 246 | 2009 |
| Towards best practice in explaining neural network decisions with LRP M Kohlbrenner, A Bauer, S Nakajima, A Binder, W Samek, S Lapuschkin 2020 international joint conference on neural networks (IJCNN), 1-7, 2020 | 244 | 2020 |
| Bayesian group-sparse modeling and variational inference SD Babacan, S Nakajima, MN Do IEEE transactions on signal processing 62 (11), 2906-2921, 2014 | 164 | 2014 |
| Asymptotically unbiased estimation of physical observables with neural samplers KA Nicoli, S Nakajima, N Strodthoff, W Samek, KR Müller, P Kessel Physical Review E 101 (2), 023304, 2020 | 163 | 2020 |
| Estimation of thermodynamic observables in lattice field theories with deep generative models KA Nicoli, CJ Anders, L Funcke, T Hartung, K Jansen, P Kessel, ... Physical review letters 126 (3), 032001, 2021 | 157 | 2021 |
| Pool-based active learning in approximate linear regression M Sugiyama, S Nakajima Machine Learning 75 (3), 249-274, 2009 | 135 | 2009 |
| Global analytic solution of fully-observed variational Bayesian matrix factorization S Nakajima, M Sugiyama, SD Babacan, R Tomioka The Journal of Machine Learning Research 14 (1), 1-37, 2013 | 134 | 2013 |
| Theoretical analysis of Bayesian matrix factorization S Nakajima, M Sugiyama The Journal of Machine Learning Research 12, 2583-2648, 2011 | 75 | 2011 |
| Support Vector Data Descriptions and -Means Clustering: One Class? N Görnitz, LA Lima, KR Müller, M Kloft, S Nakajima IEEE transactions on neural networks and learning systems 29 (9), 3994-4006, 2017 | 59 | 2017 |
| Variational Bayesian learning theory S Nakajima, K Watanabe, M Sugiyama Cambridge University Press, 2019 | 56 | 2019 |
| Perfect dimensionality recovery by variational Bayesian PCA S Nakajima, R Tomioka, M Sugiyama, S Babacan Advances in neural information processing systems 25, 2012 | 56 | 2012 |
| Noisegrad—enhancing explanations by introducing stochasticity to model weights K Bykov, A Hedström, S Nakajima, MMC Höhne Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6132-6140, 2022 | 52 | 2022 |
| XAI for graphs: explaining graph neural network predictions by identifying relevant walks T Schnake, O Eberle, J Lederer, S Nakajima, KT Schütt, KR Müller, ... arXiv preprint arXiv:2006.03589, 2020 | 52 | 2020 |
| Robustifying models against adversarial attacks by langevin dynamics V Srinivasan, C Rohrer, A Marban, KR Müller, W Samek, S Nakajima Neural Networks 137, 1-17, 2021 | 43 | 2021 |
| Ml-based qoe estimation in 5g networks using different regression techniques S Schwarzmann, CC Marquezan, R Trivisonno, S Nakajima, V Barriac, ... IEEE Transactions on Network and Service Management 19 (3), 3516-3532, 2022 | 42 | 2022 |
| Explaining bayesian neural networks K Bykov, MMC Höhne, A Creosteanu, KR Müller, F Klauschen, ... arXiv preprint arXiv:2108.10346, 2021 | 42 | 2021 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
