PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

EPrints submitted by Joris Mooij

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Number of EPrints submitted by this user: 15

Understanding and Improving Belief Propagation
Joris Mooij
(2008) PhD thesis, Radboud University Nijmegen.

Nonlinear causal discovery with additive noise models
Patrik Hoyer, Dominik Janzing, Joris Mooij, Jonas Peters and Bernhard Schölkopf
Advances in Neural Information Processing Systems Volume 21, pp. 689-696, 2009.

Bounds on marginal probability distributions
Joris Mooij and Bert Kappen
Advances in Neural Information Processing Systems Volume 21, pp. 1105-1112, 2009.

Regression by dependence minimization and its application to causal inference
Joris Mooij, Dominik Janzing, Jonas Peters and Bernhard Schölkopf
In: The 26th International Conference on Machine Learning (ICML 2009), 14-18 Jun 2009, Montreal, Canada.

Identifying confounders using additive noise models
Dominik Janzing, Jonas Peters, Joris Mooij and Bernhard Schölkopf
In: The 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), 18-21 Jun 2009, Montreal, Canada.

Remote Sensing Feature Selection by Kernel Dependence Measures
Gustavo Camps-Valls, Joris Mooij and Bernhard Schölkopf
IEEE Geoscience and Remote Sensing Letters Volume 7, Number 3, pp. 587-591, 2010. ISSN 1545-598X

Distinguishing between cause and effect
Joris Mooij and Dominik Janzing
Journal of Machine Learning Research Workshop & Conference Proceedings Volume 6, pp. 147-156, 2010. ISSN 1938-7228

Inferring deterministic causal relations
Povilas Daniušis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang and Bernhard Schölkopf
In: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), 8-11 July 2010, Catalina Island, California.

libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
Joris Mooij
Journal of Machine Learning Research Volume 11, pp. 2169-2173, 2010. ISSN ISSN 1532-4435

Probabilistic latent variable models for distinguishing between cause and effect
Joris Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang and Bernhard Schölkopf
In: Advances in Neural Information Processing Systems 23 (NIPS*2010), 6-11 Dec 2010, Vancouver, Canada.

A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller
Suzanne Martens, Joris Mooij, Jeremy Hill, Jason Farquhar and Bernhard Schölkopf
Neural Computation Volume 23, Number 1, pp. 160-182, 2011. ISSN 0899-7667

Identifiability of Causal Graphs using Functional Models
Jonas Peters, Joris Mooij, Dominik Janzing and Bernhard Schölkopf
In: 27th Annual Conference on Uncertainty in Artificial Intelligence (UAI-11), July 14-17, 2011, Barcelona, Spain.

Learning of causal relations
John Quinn, Joris Mooij, Tom Heskes and Michael Biehl
In: 19th European Symposium on Artificial Neural Networks (ESANN 2011), April 27-29, 2011, Bruges, Belgium.

Efficient inference in matrix-variate Gaussian models with iid observation noise
Oliver Stegle, Christoph Lippert, Joris Mooij, Neil Lawrence and Karsten Borgwardt
In: Advances in Neural Information Processing Systems 24 (NIPS*2011), December 12-14, 2011, Granada, Spain.

On Causal Discovery with Cyclic Additive Noise Models
Joris Mooij, Dominik Janzing, Tom Heskes and Bernhard Schölkopf
In: Advances in Neural Information Processing Systems 24 (NIPS*2011), December 12-14, 2011, Granada, Spain.