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Joris M. Mooij
Joris M. Mooij
Professor in Mathematical Statistics, Korteweg-de Vries Institute, University of Amsterdam (NL)
Zweryfikowany adres z uva.nl - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
MAGMA: Generalized Gene-Set Analysis of GWAS Data
CA de Leeuw, JM Mooij, T Heskes, D Posthuma
PLOS Computational Biology 11 (4), e1004219, 2015
31022015
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
12952009
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 30, 2017
9262017
On causal and anticausal learning
B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, J Mooij
arXiv preprint arXiv:1206.6471, 2012
7082012
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
6492014
Distinguishing cause from effect using observational data: methods and benchmarks
JM Mooij, J Peters, D Janzing, J Zscheischler, B Schölkopf
Journal of Machine Learning Research 17 (32), 1-102, 2016
6032016
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
3752012
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
3752010
Sufficient conditions for convergence of the sum–product algorithm
JM Mooij, HJ Kappen
IEEE Transactions on Information Theory 53 (12), 4422-4437, 2007
3002007
Joint causal inference from multiple contexts
JM Mooij, S Magliacane, T Claassen
Journal of machine learning research 21 (99), 1-108, 2020
2722020
Domain adaptation by using causal inference to predict invariant conditional distributions
S Magliacane, T van Ommen, T Claassen, S Bongers, P Versteeg, ...
Advances in Neural Information Processing Systems, 10846-10856, 2018
2562018
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
2242010
Foundations of structural causal models with cycles and latent variables
S Bongers, P Forré, J Peters, JM Mooij
The Annals of Statistics 49 (5), 2885-2915, 2021
2042021
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
1852011
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
175*2009
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
1712016
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
1582013
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
1582010
Causal consistency of structural equation models
PK Rubenstein, S Weichwald, S Bongers, JM Mooij, D Janzing, ...
arXiv preprint arXiv:1707.00819, 2017
1382017
From ordinary differential equations to structural causal models: the deterministic case
JM Mooij, D Janzing, B Schölkopf
Proceedings of the 29th Annual Conference on Uncertainty in Artificial …, 2013
1272013
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