Kun Zhang
Kun Zhang
Carnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
Verified email at - Homepage
Cited by
Cited by
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
Review of causal discovery methods based on graphical models
C Glymour, K Zhang, P Spirtes
Frontiers in genetics 10, 524, 2019
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
Domain adaptation under target and conditional shift
K Zhang, B Schölkopf, K Muandet, Z Wang
International conference on machine learning, 819-827, 2013
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
Kernel-based conditional independence test and application in causal discovery
K Zhang, J Peters, D Janzing, B Schölkopf
arXiv preprint arXiv:1202.3775, 2012
On causal and anticausal learning
B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, J Mooij
arXiv preprint arXiv:1206.6471, 2012
On learning invariant representations for domain adaptation
H Zhao, RT Des Combes, K Zhang, G Gordon
International conference on machine learning, 7523-7532, 2019
On the identifiability of the post-nonlinear causal model
K Zhang, A Hyvarinen
arXiv preprint arXiv:1205.2599, 2012
Multi-label learning by exploiting label dependency
ML Zhang, K Zhang
Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010
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
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
Causal discovery and inference: concepts and recent methodological advances
P Spirtes, K Zhang
Applied informatics 3, 1-28, 2016
Information-geometric approach to inferring causal directions
D Janzing, J Mooij, K Zhang, J Lemeire, J Zscheischler, P Daniu¹is, ...
Artificial Intelligence 182, 1-31, 2012
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
Multi-source domain adaptation: A causal view
K Zhang, M Gong, B Schölkopf
Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015
Inferring deterministic causal relations
P Daniusis, D Janzing, J Mooij, J Zscheischler, B Steudel, K Zhang, ...
arXiv preprint arXiv:1203.3475, 2012
Causal discovery from heterogeneous/nonstationary data
B Huang, K Zhang, J Zhang, J Ramsey, R Sanchez-Romero, C Glymour, ...
Journal of Machine Learning Research 21 (89), 1-53, 2020
Approximate kernel-based conditional independence tests for fast non-parametric causal discovery
EV Strobl, K Zhang, S Visweswaran
Journal of Causal Inference 7 (1), 20180017, 2019
Model selection for Gaussian mixture models
T Huang, H Peng, K Zhang
Statistica Sinica, 147-169, 2017
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