Joris M. Mooij
Joris M. Mooij
Professor in Mathematical Statistics, Korteweg-de Vries Institute, University of Amsterdam (NL)
Verified email at uva.nl - Homepage
Title
Cited by
Cited by
Year
MAGMA: Generalized Gene-Set Analysis of GWAS Data
CA de Leeuw, JM Mooij, T Heskes, D Posthuma
PLOS Computational Biology 11 (4), e1004219, 2015
9912015
Nonlinear causal discovery with additive noise models
PO Hoyer, D Janzing, JM Mooij, J Peters, B Schölkopf
Advances in neural information processing systems (NIPS*2008), 689-696, 2009
5662009
libDAI: A free and open source C++ library for discrete approximate inference in graphical models
JM Mooij
The Journal of Machine Learning Research 11, 2169-2173, 2010
3282010
Sufficient conditions for convergence of the sum–product algorithm
JM Mooij, HJ Kappen
IEEE Transactions on Information Theory 53 (12), 4422-4437, 2007
2712007
Distinguishing cause from effect using observational data: methods and benchmarks
JM Mooij, J Peters, D Janzing, J Zscheischler, B Schölkopf
The Journal of Machine Learning Research 17 (1), 1103-1204, 2016
2682016
Causal discovery with continuous additive noise models
J Peters, JM Mooij, D Janzing, B Schölkopf
The Journal of Machine Learning Research 15 (1), 2009-2053, 2014
2552014
On causal and anticausal learning
B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, J Mooij
arXiv preprint arXiv:1206.6471, 2012
2342012
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
2102012
Causal effect inference with deep latent-variable models
C Louizos, U Shalit, JM Mooij, D Sontag, R Zemel, M Welling
Advances in neural information processing systems, 6446-6456, 2017
1832017
Inferring deterministic causal relations
P Daniušis, D Janzing, J Mooij, J Zscheischler, B Steudel, K Zhang, ...
Proceedings of the 26th Annual Conference on Uncertainty in Artificial …, 2010
1422010
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 (NIPS*2010), 1687-1695, 2010
1022010
Identifiability of causal graphs using functional models
J Peters, J Mooij, D Janzing, B Schölkopf
Proceedings of the 27th Annual Conference on Uncertainty in Artificial …, 2011
992011
Regression by dependence minimization and its application to causal inference
J Mooij, D Janzing, J Peters, B Schölkopf
Proceedings of the 26th Annual International Conference on Machine Learning …, 2009
99*2009
Remote sensing feature selection by kernel dependence measures
G Camps-Valls, J Mooij, B Scholkopf
IEEE Geoscience and Remote Sensing Letters 7 (3), 587-591, 2010
982010
Efficient inference in matrix-variate Gaussian models with iid observation noise
O Stegle, C Lippert, J Mooij, N Lawrence, K Borgwardt
Advances in Neural Information Processing Systems 23 (NIPS*2010), 1687--1695, 2011
912011
Learning sparse causal models is not NP-hard
T Claassen, J Mooij, T Heskes
Proceedings of the 29th Annual Conference on Uncertainty in Artificial …, 2013
712013
On causal discovery with cyclic additive noise models
JM Mooij, D Janzing, T Heskes, B Schölkopf
Advances in Neural Information Processing Systems (NIPS*2011), 639-647, 2011
712011
On the properties of the Bethe approximation and loopy belief propagation on binary networks
JM Mooij, HJ Kappen
Journal of Statistical Mechanics: Theory and Experiment 2005 (11), P11012, 2005
682005
Methods for causal inference from gene perturbation experiments and validation
N Meinshausen, A Hauser, JM Mooij, J Peters, P Versteeg, P Bühlmann
Proceedings of the National Academy of Sciences 113 (27), 7361-7368, 2016
672016
Sufficient conditions for convergence of loopy belief propagation
J Mooij, H Kappen
Proceedings of the 21st Annual Conference on Uncertainty in Artificial …, 2005
66*2005
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Articles 1–20