| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1369 | 2018 |
| Domain adaptation under target and conditional shift K Zhang, B Schölkopf, K Muandet, Z Wang International conference on machine learning, 819-827, 2013 | 589 | 2013 |
| Multi-label learning by exploiting label dependency ML Zhang, K Zhang Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010 | 546 | 2010 |
| Review of causal discovery methods based on graphical models C Glymour, K Zhang, P Spirtes Frontiers in genetics 10, 524, 2019 | 543 | 2019 |
| Kernel-based conditional independence test and application in causal discovery K Zhang, J Peters, D Janzing, B Schölkopf arXiv preprint arXiv:1202.3775, 2012 | 528 | 2012 |
| Deep domain generalization via conditional invariant adversarial networks Y Li, X Tian, M Gong, Y Liu, T Liu, K Zhang, D Tao Proceedings of the European conference on computer vision (ECCV), 624-639, 2018 | 493 | 2018 |
| On causal and anticausal learning B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, J Mooij arXiv preprint arXiv:1206.6471, 2012 | 486 | 2012 |
| On causal and anticausal learning B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, J Mooij arXiv preprint arXiv:1206.6471, 2012 | 486 | 2012 |
| On the identifiability of the post-nonlinear causal model K Zhang, A Hyvarinen arXiv preprint arXiv:1205.2599, 2012 | 472 | 2012 |
| Inferring causation from time series in Earth system sciences J Runge, S Bathiany, E Bollt, G Camps-Valls, D Coumou, E Deyle, ... Nature communications 10 (1), 2553, 2019 | 463 | 2019 |
| On learning invariant representations for domain adaptation H Zhao, RT Des Combes, K Zhang, G Gordon International conference on machine learning, 7523-7532, 2019 | 454 | 2019 |
| Domain adaptation with conditional transferable components M Gong, K Zhang, T Liu, D Tao, C Glymour, B Schölkopf International conference on machine learning, 2839-2848, 2016 | 323 | 2016 |
| Information-geometric approach to inferring causal directions D Janzing, J Mooij, K Zhang, J Lemeire, J Zscheischler, P Daniušis, ... Artificial Intelligence 182, 1-31, 2012 | 316 | 2012 |
| Estimation of a structural vector autoregression model using non-gaussianity. A Hyvärinen, K Zhang, S Shimizu, PO Hoyer Journal of Machine Learning Research 11 (5), 2010 | 306 | 2010 |
| Causal discovery and inference: concepts and recent methodological advances P Spirtes, K Zhang Applied informatics 3 (1), 1-28, 2016 | 298 | 2016 |
| Inferring deterministic causal relations P Daniusis, D Janzing, J Mooij, J Zscheischler, B Steudel, K Zhang, ... arXiv preprint arXiv:1203.3475, 2012 | 186 | 2012 |
| Multi-source domain adaptation: A causal view K Zhang, M Gong, B Schölkopf Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 176 | 2015 |
| Geometry-consistent generative adversarial networks for one-sided unsupervised domain mapping H Fu, M Gong, C Wang, K Batmanghelich, K Zhang, D Tao Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 149 | 2019 |
| Probabilistic latent variable models for distinguishing between cause and effect O Stegle, D Janzing, K Zhang, JM Mooij, B Schölkopf Advances in neural information processing systems 23, 2010 | 136 | 2010 |
| Model selection for Gaussian mixture models T Huang, H Peng, K Zhang Statistica Sinica, 147-169, 2017 | 135 | 2017 |
