Publikationen von D Janzing

Konferenzbeitrag (27)

21.
Konferenzbeitrag
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), S. 589 - 598 (Hg. Cozman, F.; Pfeffer, A.). 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain. AUAI Press, Corvallis, OR, USA (2011)
22.
Konferenzbeitrag
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), S. 804 - 813 (Hg. Cozman, F.; Pfeffer, A.). 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain. AUAI Press, Corvallis, OR, USA (2011)
23.
Konferenzbeitrag
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), S. 839 - 847 (Hg. Cozman, F.; Pfeffer, A.). 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain. AUAI Press, Corvallis, OR, USA (2011)
24.
Konferenzbeitrag
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, 06. Dezember 2010 - 11. Dezember 2010. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, S. 1687 - 1695 (2011)
25.
Konferenzbeitrag
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), S. 143 - 150 (Hg. Grünwald, P.; Spirtes, P.). 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), Catalina Island, CA, USA, 08. Juli 2010 - 11. Juli 2010. AUAI Press, Corvallis, OR, USA (2010)
26.
Konferenzbeitrag
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), S. 717 - 724 (Hg. Grünwald, P.; Spirtes, P.). 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), Catalina Island, CA, USA, 08. Juli 2010 - 11. Juli 2010. AUAI Press, Corvallis, OR, USA (2010)
27.
Konferenzbeitrag
Janzing, D.; Hoyer, P.; Schölkopf, B.: Telling cause from effect based on high-dimensional observations. In: 27th International Conference on Machine Learning (ICML 2010), S. 479 - 486 (Hg. Fürnkranz, J.; Joachims, T.). 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, 21. Juni 2010 - 24. Juni 2010. Omnipress, Madison, WI, USA (2010)
28.
Konferenzbeitrag
Steudel, B.; Janzing, D.; Schölkopf, B.: Causal Markov condition for submodular information measures. In: 23rd Annual Conference on Learning Theory (COLT 2010), S. 464 - 476 (Hg. Tauman Kalai, A.; Mohri, M.). 23rd Annual Conference on Learning Theory (COLT 2010), Haifa, Israel, 27. Juni 2010 - 29. Juni 2010. OmniPress, Madison, WI, USA (2010)
29.
Konferenzbeitrag
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, 13. Mai 2010 - 15. Mai 2010. JMLR Workshop and Conference Proceedings 9, S. 597 - 604 (2010)
30.
Konferenzbeitrag
Mooij, J.; Janzing, D.: Distinguishing between cause and effect. NIPS 2008 Workshop: Causality: Objectives and Assessment, Whistler, BC, Canada, 12. Dezember 2008. JMLR Workshop and Conference Proceedings 6, S. 147 - 156 (2010)
31.
Konferenzbeitrag
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, 16. Juli 2008 - 18. Juli 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, S. 57 - 66 (2010)
32.
Konferenzbeitrag
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, S. 689 - 696 (Hg. Koller, D.; Schuurmans, D.; Bengio, Y.; Bottou, L.). Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Vancouver, BC, Canada, 08. Dezember 2008 - 10. Dezember 2008. Curran, Red Hook, NY, USA (2009)
33.
Konferenzbeitrag
Janzing, D.; Peters, J.; Mooij, J.; Schölkopf, B.: Identifying confounders using additive noise models. In: 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), S. 249 - 257 (Hg. Bilmes, N.; Ng, A.; McAllester, D.). 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), Montréal, Canada, 18. Juni 2009 - 21. Juni 2009. AUAI Press, Corvallis, OR, USA (2009)
34.
Konferenzbeitrag
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, S. 745 - 752 (Hg. Danyluk, A.; Bottou, L.; Littman, M.). 26th International Conference on Machine Learning (ICML 2009), Montreal, Canada, 14. Juni 2009 - 18. Juni 2009. ACM Press, New York, NY, USA (2009)
35.
Konferenzbeitrag
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, S. 801 - 808 (Hg. Danyluk, A.; Bottou, L.; Danyluk, M.). 26th International Conference on Machine Learning, Montreal, Canada, 14. Juni 2009 - 18. Juni 2009. ACM Press, New York, NY, USA (2009)
36.
Konferenzbeitrag
Sun, X.; Janzing, D.; Schölkopf, B.; Fukumizu, K.: A Kernel-Based Causal Learning Algorithm. In: ICML '07: 24th International Conference on Machine Learning, S. 855 - 862 (Hg. Ghahramani, Z.). 24th Annual International Conference on Machine Learning (ICML 2007), Corvallis, OR, USA, 20. Juni 2007 - 24. Juni 2007. ACM Press, New York, NY, USA (2007)
37.
Konferenzbeitrag
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, S. 465 - 470 (Hg. Verleysen, M.). 15th European Symposium on Artificial Neural Networks (ESANN 2007), Brugge, Belgium, 25. April 2007 - 27. April 2007. D-Side, Evere, Belgium (2007)
38.
Konferenzbeitrag
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, S. 453 - 458 (Hg. Verleysen, M.). 15th European Symposium on Artificial Neural Networks (ESANN 2007), Brugge, Belgium, 25. April 2007 - 27. April 2007. D-Side, Evere, Belgium (2007)
39.
Konferenzbeitrag
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, S. 441 - 446 (Hg. Verleysen, M.). 15th European Symposium on Artificial Neural Networks (ESANN 2007), Brugge, Belgium, 25. April 2007 - 27. April 2007. D-Side Publications, Evere, Belgium (2007)
40.
Konferenzbeitrag
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), S. 1 - 11. Ninth International Symposium on Artificial Intelligence and Mathematics (AIMath 2006), Fort Lauderdale, FL, USA, 04. Januar 2005 - 06. Januar 2005. (2006)
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