Publications of D Janzing

Conference Paper (27)

21.
Conference Paper
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)
22.
Conference Paper
Zhang, K.; Peters, J.; Janzing, D.; Schölkopf, B.: Kernel-based Conditional Independence Test and Application in Causal Discovery. In: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pp. 804 - 813 (Eds. Cozman, F.; Pfeffer, A.). 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain. AUAI Press, Corvallis, OR, USA (2011)
23.
Conference Paper
Zscheischler, J.; Janzing, D.; Zhang, K.: Testing whether linear equations are causal: A free probability theory approach. In: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pp. 839 - 847 (Eds. Cozman, F.; Pfeffer, A.). 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain. AUAI Press, Corvallis, OR, USA (2011)
24.
Conference Paper
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)
25.
Conference Paper
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)
26.
Conference Paper
Zhang, K.; Schölkopf, B.; Janzing, D.: Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. In: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), pp. 717 - 724 (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)
27.
Conference Paper
Janzing, D.; Hoyer, P.; Schölkopf, B.: Telling cause from effect based on high-dimensional observations. In: 27th International Conference on Machine Learning (ICML 2010), pp. 479 - 486 (Eds. Fürnkranz, J.; Joachims, T.). 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, June 21, 2010 - June 24, 2010. Omnipress, Madison, WI, USA (2010)
28.
Conference Paper
Steudel, B.; Janzing, D.; Schölkopf, B.: Causal Markov condition for submodular information measures. In: 23rd Annual Conference on Learning Theory (COLT 2010), pp. 464 - 476 (Eds. Tauman Kalai, A.; Mohri, M.). 23rd Annual Conference on Learning Theory (COLT 2010), Haifa, Israel, June 27, 2010 - June 29, 2010. OmniPress, Madison, WI, USA (2010)
29.
Conference Paper
Peters, J.; Janzing, D.; Schölkopf, B.: Identifying Cause and Effect on Discrete Data using Additive Noise Models. Thirteenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2010), Chia Laguna Resort, Italy, May 13, 2010 - May 15, 2010. JMLR Workshop and Conference Proceedings 9, pp. 597 - 604 (2010)
30.
Conference Paper
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)
31.
Conference Paper
Peters, J.; Janzing, D.; Gretton, A.; Schölkopf, B.: Kernel Methods for Detecting the Direction of Time Series. 32nd Annual Conference of the Gesellschaft für Klassifikation e.V. (GfKl 2008), Hamburg, Germany, July 16, 2008 - July 18, 2008. Advances in Data Analysis, Data Handling and Business Intelligence: Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC), Helmut-Schmidt-University, Hamburg, July 16-18, 2008, pp. 57 - 66 (2010)
32.
Conference Paper
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)
33.
Conference Paper
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)
34.
Conference Paper
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)
35.
Conference Paper
Peters, J.; Janzing, D.; Gretton, A.; Schölkopf, B.: Detecting the Direction of Causal Time Series. In: ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 801 - 808 (Eds. Danyluk, A.; Bottou, L.; Danyluk, M.). 26th International Conference on Machine Learning, Montreal, Canada, June 14, 2009 - June 18, 2009. ACM Press, New York, NY, USA (2009)
36.
Conference Paper
Sun, X.; Janzing, D.; Schölkopf, B.; Fukumizu, K.: A Kernel-Based Causal Learning Algorithm. In: ICML '07: 24th International Conference on Machine Learning, pp. 855 - 862 (Ed. Ghahramani, Z.). 24th Annual International Conference on Machine Learning (ICML 2007), Corvallis, OR, USA, June 20, 2007 - June 24, 2007. ACM Press, New York, NY, USA (2007)
37.
Conference Paper
Sun, X.; Janzing, D.: Exploring the causal order of binary variables via exponential hierarchies of Markov kernels. In: Advances in computational intelligence and learning: 15th European Symposium on Artificial Neural Networks: ESANN 2007, pp. 465 - 470 (Ed. Verleysen, M.). 15th European Symposium on Artificial Neural Networks (ESANN 2007), Brugge, Belgium, April 25, 2007 - April 27, 2007. D-Side, Evere, Belgium (2007)
38.
Conference Paper
Sun, X.; Janzing, D.: Learning causality by identifying common effects with kernel-based dependence measures. In: Advances in computational intelligence and learning: 15th European Symposium on Artificial Neural Networks: ESANN 2007, pp. 453 - 458 (Ed. Verleysen, M.). 15th European Symposium on Artificial Neural Networks (ESANN 2007), Brugge, Belgium, April 25, 2007 - April 27, 2007. D-Side, Evere, Belgium (2007)
39.
Conference Paper
Sun, X.; Janzing, D.; Schölkopf, B.: Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions. In: Advances in computational intelligence and learning: 15th European Symposium on Artificial Neural Networks: ESANN 2007, pp. 441 - 446 (Ed. Verleysen, M.). 15th European Symposium on Artificial Neural Networks (ESANN 2007), Brugge, Belgium, April 25, 2007 - April 27, 2007. D-Side Publications, Evere, Belgium (2007)
40.
Conference Paper
Sun, X.; Janzing, D.; Schölkopf, B.: Causal Inference by Choosing Graphs with Most Plausible Markov Kernels. In: Ninth International Symposium on Artificial Intelligence and Mathematics (AIMath 2006), pp. 1 - 11. Ninth International Symposium on Artificial Intelligence and Mathematics (AIMath 2006), Fort Lauderdale, FL, USA, January 04, 2005 - January 06, 2005. (2006)
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