Articles (4): |
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Janzing D  , Mooij J , Zhang K , Lemeire J , Zscheischler J , Daniušis P , Steudel B and Schölkopf B (2012) Information-geometric approach to inferring causal directions
Artificial Intelligence 182-183 1-31.
 
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Martens SMM , Mooij JM , Hill NJ , Farquhar J and Schölkopf B (2011) A graphical model framework for decoding in the visual ERP-based BCI speller
Neural Computation 23(1) 160-182.
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Mooij JM (2010) libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
Journal of Machine Learning Research 11 2169-2173.
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Camps-Valls G , Mooij JM and Schölkopf B (2010) Remote Sensing Feature Selection by Kernel Dependence Estimation
IEEE Geoscience and Remote Sensing Letters 7(3) 587-591.
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Conference papers (12): |
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Mooij J , Janzing D and Schölkopf B (2013) From Ordinary Differential Equations to Structural Causal Models: the deterministic case
29th Conference on Uncertainty in Artificial Intelligence (UAI 2013).
accepted
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Schölkopf B , Janzing D , Peters J , Sgouritsa E , Zhang K and Mooij J (2012) On Causal and Anticausal Learning
In: Proceedings of the 29th International Conference on Machine Learning (ICML 2012), (Ed) J Langford and J Pineau, 29th International Conference on Machine Learning (ICML 2012), Omnipress, New York, NY, USA, 1255-1262.
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Peters J , Mooij J , Janzing D and Schölkopf B (2011) Identifiability of causal graphs using functional models
(Ed) FG Cozman and A Pfeffer, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 589-598.
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Stegle O , Lippert C , Mooij J , Lawrence N and Borgwardt K (2011) Efficient inference in matrix-variate Gaussian models with iid observation noise
In: Advances in Neural Information Processing Systems 24, (Ed) J Shawe-Taylor, RS Zemel, P Bartlett, F Pereira and KQ Weinberger, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), 630-638.
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Mooij J , Janzing D , Schölkopf B and Heskes T (2011) On Causal Discovery with Cyclic Additive Noise Models
In: Advances in Neural Information Processing Systems 24, (Ed) J Shawe-Taylor, RS Zemel, PL Bartlett, FCN Pereira and KQ Weinberger, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Curran Associates, Inc., Red Hook, NY, USA, 639-647.
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Daniusis P , Janzing D , Mooij J , Zscheischler J , Steudel B , Zhang K and Schölkopf B (2010) Inferring deterministic causal relations
In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, (Ed) P Grünwald and P Spirtes, UAI 2010, AUAI Press, Corvallis, OR, USA, 143-150.
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Mooij J and Janzing D (2010) Distinguishing between cause and effect
In: JMLR Workshop and Conference Proceedings: Volume 6, (Ed) Guyon, I. , D. Janzing, B. Schölkopf, Causality: Objectives and Assessment (NIPS 2008 Workshop), MIT Press, Cambridge, MA, USA, 147-156.
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Mooij JM , Stegle O , Janzing D , Zhang K and Schölkopf B (2010) Probabilistic latent variable models for distinguishing between cause and effect
In: Advances in Neural Information Processing Systems 23, (Ed) J Lafferty, CKI Williams, J Shawe-Taylor, RS Zemel and A Culotta, 24th Annual Conference on Neural Information Processing Systems (NIPS 2010), Curran, Red Hook, NY, USA, 1687-1695.
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Mooij JM and Kappen B (2009) Bounds on marginal probability distributions
In: Advances in neural information processing systems 21, (Ed) Koller, D. , D. Schuurmans, Y. Bengio, L. Bottou, Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 1105-1112.
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Janzing D , Peters J , Mooij JM and Schölkopf B (2009) Identifying confounders using additive noise models
In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, (Ed) J Bilmes and AY Ng, UAI 2009, AUAI Press, Corvallis, OR, USA, 249-257.
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Hoyer PO , Janzing D , Mooij JM , Peters J and Schölkopf B (2009) Nonlinear causal discovery with additive noise models
In: Advances in neural information processing systems 21, (Ed) D Koller, D Schuurmans, Y Bengio and L Bottou, 22nd Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 689-696.
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Mooij JM , Janzing D , Peters J and Schölkopf B (2009) Regression by dependence minimization and its application to causal inference in additive noise models
In: Proceedings of the 26th International Conference on Machine Learning, (Ed) A Danyluk, L Bottou and M Littman, ICML 2009, ACM Press, New York, NY, USA, 745-752.
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joris.mooij
