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On Causal Discovery with Cyclic Additive Noise Models
J. M. Mooij, D. Janzing, T. Heskes, B. Schölkopf
Advances in Neural Information Processing Systems 24 (
NIPS*2011)
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Efficient inference in matrix-variate Gaussian models with iid observation noise
O. Stegle, C. Lippert, J. M. Mooij, N. Lawrence, K. Borgwardt
Advances in Neural Information Processing Systems 24 (
NIPS*2011)
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Learning of causal relations
J. Quinn, J. M. Mooij, T. Heskes, M. Biehl
Proceedings of the 19th European Symposium on Artificial Neural Networks (
ESANN 2011)
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Identifiability of Causal Graphs using Functional Models
J. Peters, J. M. Mooij, D. Janzing, B. Schölkopf
Proceedings of the 27th Annual Conference on Uncertainty in Artificial Intelligence (UAI-11) (
UAI 2011)
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A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller
S. M. M. Martens, J. M. Mooij, N. J. Hill, J. Farquhar, B. Schölkopf
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Probabilistic latent variable models for distinguishing between cause and effect
J. M. Mooij, O. Stegle, D. Janzing, K. Zhang, B. Schölkopf
Advances in Neural Information Processing Systems 23 (
NIPS*2010)
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libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
Joris M. Mooij
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Inferring deterministic causal relations
P. Daniušis, D. Janzing, J. Mooij, J. Zscheischler, B. Steudel, K. Zhang, B. Schölkopf
Proceedings of the 26th Annual Conference on Uncertainty in Artificial Intelligence (UAI-10) (
UAI 2010)
(Best student paper award)
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Remote Sensing Feature Selection by Kernel Dependence Measures
G. Camps-Valls, J. M. Mooij, B. Schölkopf
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Distinguishing between cause and effect
J. M. Mooij, D. Janzing
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Identifying confounders using additive noise models
D. Janzing, J. Peters, J. M. Mooij, B. Schölkopf
Proceedings of the 25th Annual Conference on Uncertainty in Artificial Intelligence (UAI-09) (
UAI 2009)
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Regression by dependence minimization and its application to causal inference
J. M. Mooij, D. Janzing, J. Peters, B. Schölkopf
Proceedings of the 26th Annual International Conference on Machine Learning (ICML 2009) (
ICML 2009)
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Nonlinear causal discovery with additive noise models
P. Hoyer, D. Janzing, J. Mooij, J. Peters, B. Schölkopf
Advances in Neural Information Processing Systems 21 (
NIPS*2008)
(Corollary 2 which appeared in a previous version of this paper has been removed because it contained an error)
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Bounds on marginal probability distributions
J.M. Mooij, H.J. Kappen
Advances in Neural Information Processing Systems 21 (
NIPS*2008)
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Understanding and Improving Belief Propagation
J.M. Mooij
PhD thesis May 7, 2008
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Sufficient Conditions for Convergence of the Sum-Product Algorithm
J.M. Mooij, H.J. Kappen
IEEE Transactions on Information Theory 53(12):4422-4437, Dec. 2007
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Truncating the Loop Series Expansion for Belief Propagation
Vicenç Gómez, J. M. Mooij, H. J. Kappen
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Loop Corrections for Approximate Inference on Factor Graphs
Joris M. Mooij, Hilbert J. Kappen
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Inference in the Promedas medical expert system
Bastian Wemmenhove, Joris M. Mooij, Wim Wiegerinck, Martijn Leisink, Hilbert J. Kappen, Jan P. Neijt
Proceedings of the 11th Conference on Artificial Intelligence in Medicine (
AIME 07)
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Loop Corrected Belief Propagation
J.M. Mooij, B. Wemmenhove, H.J. Kappen, T. Rizzo
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (
AISTATS-07)
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Sufficient conditions for convergence of Loopy Belief Propagation
J.M. Mooij, H.J. Kappen
Proceedings of the 21th Annual Conference on Uncertainty in Artificial Intelligence (
UAI-05)
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On the properties of the Bethe approximation and Loopy Belief Propagation on binary networks
J.M. Mooij, H.J. Kappen
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Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks
J.M. Mooij, H.J. Kappen
Advances in Neural Information Processing Systems 17 (
NIPS*2004)
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Quantitative Imaging through a Spectrograph. 1. Principles and Theory
René Tolboom, Nico Dam, Hans ter Meulen, Joris Mooij, Hans Maassen
