Publications of J Mooij

Janzing, D.; Mooij, J.; Zhang, K.; Lemeire, J.; Zscheischler, J.; Daniušis, P.; Steudel, B.; Schölkopf, B.: Information-geometric approach to inferring causal directions. Artificial Intelligence 182-183, pp. 1 - 31 (2012)

Martens, S.; Mooij, J.; Hill, N.; Farquhar, J.; Schölkopf, B.: A graphical model framework for decoding in the visual ERP-based BCI speller. Neural computation 23 (1), pp. 160 - 182 (2011)

Mooij, J.: libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models. The Journal of Machine Learning Research 11, pp. 2169 - 2173 (2010)

Camps-Valls, G.; Mooij, J.; Schölkopf, B.: Remote Sensing Feature Selection by Kernel Dependence Estimation. IEEE Geoscience and Remote Sensing Letters 7 (3), pp. 587 - 591 (2010)

Schölkopf, B.; Janzing, D.; Peters, J.; Sgouritsa, E.; Zhang, K.; Mooij, J.: On causal and anticausal learning. In: 29th International Conference on Machine Learning (ICML 2012), pp. 1255 - 1262 (Eds. Langford, J.; Pineau, J.). 29th International Conference on Machine Learning (ICML 2012), Edinburgh, UK. International Machine Learning Society, Madison, WI, USA (2012)

Mooij, J.; Janzing, D.; Heskes, T.; Schölkopf, B.: On Causal Discovery with Cyclic Additive Noise Models. In: Advances in Neural Information Processing Systems 24, pp. 639 - 647 (Eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F.; Weinberger, K.). Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Granada, Spain. Curran, Red Hook, NY, USA (2012)

Stegle, O.; Lippert, C.; Mooij, J.; Lawrence, N.; Borgwardt, K.: Efficient inference in matrix-variate Gaussian models with iid observation noise. In: Advances in Neural Information Processing Systems 24, pp. 630 - 638 (Eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F.; Weinberger, K.). Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Granada, Spain. Curran, Red Hook, NY, USA (2012)

Peters, J.; Mooij, J.; Janzing, D.; Schölkopf, B.: Identifiability of causal graphs using functional models. In: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pp. 589 - 598 (Eds. Cozman, F.; Pfeffer, A.). 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain. AUAI Press, Corvallis, OR, USA (2011)

Mooij, J.; Stegle, O.; Janzing, D.; Zhang, K.; Schölkopf, B.: Probabilistic latent variable models for distinguishing between cause and effect. Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS 2010), Vancouver, BC, Canada, December 06, 2010 - December 11, 2010. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, pp. 1687 - 1695 (2011)

Daniusis, P.; Janzing, D.; Mooij, J.; Zscheischler, J.; Steudel, B.; Zhang, K.; Schölkopf, B.: Inferring deterministic causal relations. In: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), pp. 143 - 150 (Eds. Grünwald, P.; Spirtes, P.). 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), Catalina Island, CA, USA, July 08, 2010 - July 11, 2010. AUAI Press, Corvallis, OR, USA (2010)

Mooij, J.; Janzing, D.: Distinguishing between cause and effect. NIPS 2008 Workshop: Causality: Objectives and Assessment, Whistler, BC, Canada, December 12, 2008. JMLR Workshop and Conference Proceedings 6, pp. 147 - 156 (2010)

Hoyer, P.; Janzing, D.; Mooij, J.; Peters, J.; Schölkopf, B.: Nonlinear causal discovery with additive noise models. In: Advances in neural information processing systems 21, pp. 689 - 696 (Eds. Koller, D.; Schuurmans, D.; Bengio, Y.; Bottou, L.). Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Vancouver, BC, Canada, December 08, 2008 - December 10, 2008. Curran, Red Hook, NY, USA (2009)

Janzing, D.; Peters, J.; Mooij, J.; Schölkopf, B.: Identifying confounders using additive noise models. In: 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), pp. 249 - 257 (Eds. Bilmes, N.; Ng, A.; McAllester, D.). 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), Montréal, Canada, June 18, 2009 - June 21, 2009. AUAI Press, Corvallis, OR, USA (2009)

Mooij, J.; Janzing, D.; Peters, J.; Schölkopf, B.: Regression by dependence minimization and its application to causal inference in additive noise models. In: ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 745 - 752 (Eds. Danyluk, A.; Bottou, L.; Littman, M.). 26th International Conference on Machine Learning (ICML 2009), Montreal, Canada, June 14, 2009 - June 18, 2009. ACM Press, New York, NY, USA (2009)

Mooij, J.; Kappen, B.: Bounds on marginal probability distributions. In: Advances in neural information processing systems 21, pp. 1105 - 1112 (Eds. Koller, D.; Schuurmans, D.; Bengio, Y.; Bottou, L.). Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Vancouver, BC, Canada, December 08, 2008 - December 10, 2008. Curran, Red Hook, NY, USA (2009)

Mooij, J.; Janzing, D.; Peters, J.; Schölkopf, B.; Hoyer, P.: Additive noise models for causal inference. In Dagstuhl Reports, 09401, p. 10 (Eds. Janzing, D.; Lauritzen, B.; Schölkopf, B.). Dagstuhl Seminar: Machine learning approaches to statistical dependences and causality, Schloss Dagstuhl, Germany, September 27, 2009 - October 02, 2009. Schloss Dagstuhl, Leibniz-Zentrum für Informatik, Wadern (2009)

Mooij, J.: libDAI. NIPS 2008 Workshop: Machine Learning Open-Source Software, Whistler, BC, Canada (2008)