Publikationen von B Schölkopf

Machine learning approaches to statistical dependences and causality (Dagstuhl Reports, 09401). Dagstuhl Seminar: Machine learning approaches to statistical dependences and causality , Schloss Dagstuhl, Germany, 27. September 2009 - 02. Oktober 2009. (2009)

Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference. Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006), Vancouver, BC, Canada, 04. Dezember 2007 - 07. Dezember 2007. (2007), 1690 S.

Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference. Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005), Vancouver, BC, Canada, 05. Dezember 2005 - 08. Dezember 2005. MIT Press, Cambridge, MA, USA (2006), 1676 S.

Pattern Recognition: 26th DAGM Symposium: Tübingen, Germany, August 30 - September 1, 2004 (Lecture Notes in Computer Science, 3175). 26th Pattern Recognition Symposium, Tübingen, Germany, 30. August 2004 - 01. September 2004. Springer, Berlin, Germany (2004), 581 S.

Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference. Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), Vancouver, BC, Canada, 08. Dezember 2003 - 13. Dezember 2003. MIT Press, Cambridge, MA, USA (2004), 1621 S.

Learning theory and Kernel machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003) (Lecture Notes in Computer Science, 2777). 16th Annual Conference on Learning Theory and 7th Kernel Workshop (COLT/Kernel 2003), Washington, DC, USA, 24. August 2003 - 27. August 2003. Springer, Berlin, Germany (2003), 746 S.

Inference Principles and Model Selection (Dagstuhl Reports, 01301). Inference Principles and Model Selection: Dagstuhl Seminar 01301, Dagstuhl, Germany, 23. Juli 2001 - 27. Juli 2001. Leibniz-Zentrum für Informatik, Dagstul, Germany (2001)

Advances in Kernel Methods: Support Vector Learning. Eleventh Annual Conference on Neural Information Processing (NIPS 1997), Breckenridge, CO, USA, 01. Dezember 1997 - 06. Dezember 1997. MIT Press, Cambridge, MA, USA (1999), 376 S.

Besserve, M.; Shajarisales, N.; Janzing, D.; Schölkopf, B.: Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations. In: Proceedings of Machine Learning Research (PMLR), Bd. 177, S. 110 - 143. 1st Conference on Causal Learning and Reasoning (CLeaR 2022), Eureka, CA, USA, 11. April 2022 - 13. April 2022. Curran, Red Hook, NY, USA (2022)

Besserve, M.; Sun, R.; Janzing, D.; Schölkopf, B.: A theory of independent mechanisms for extrapolation in generative models. In: Proceedings of the AAAI Conference on Artificial Intelligence, Bd. 35, S. 6741 - 6749. 35th AAAI Conference on Artificial Intelligence: A Virtual Conference, 02. Februar 2021 - 09. Februar 2021. (2021)

Parascandolo, G.; Neitz, A.; Orvieto, A.; Gresele, L.; Schölkopf, B.: Learning explanations that are hard to vary. In: Ninth International Conference on Learning Representations (ICLR 2021). Ninth International Conference on Learning Representations (ICLR 2021), Wien, Austria, 03. Mai 2021 - 07. Mai 2021. (2021)

Gondal, M.; Wüthrich, M.; Miladinovic, D.; Locatello, F.; Breidt, M.; Volchkov, V.; Akpo, J.; Bachem, O.; Schölkopf, B.; Bauer, S.: On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. In: Advances in Neural Information Processing Systems 32 (NeurIPS 2019), S. 15661 - 15672 (Hg. Wallach, H.; Larochelle, H.; Beygelzimer, A.; d'Alché-Buc, F.; Fox, E. et al.). 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 09. Dezember 2019 - 13. Dezember 2019. Curran, Red Hook, NY, USA (2020)

Besserve, M.; Mehrjou, A.; Sun, R.; Schölkopf, B.: Counterfactuals uncover the modular structure of deep generative models. In: Eighth International Conference on Learning Representations (ICLR 2020). Eighth International Conference on Learning Representations (ICLR 2020), Addis Ababa, Ethiopia, 26. April 2020 - 30. April 2020. (2020)

Geiger, P.; Besserve, M.; Winkelmann, J.; Proissl, C.; Schölkopf, B.: Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. In: 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), 49, S. 286 - 295. 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, 22. Juli 2019 - 25. Juli 2019. Curran, Red Hook, NY, USA (2019)

Gresele, L.; Rubenstein, P.; Mehrjou, A.; Locatello, F.; Schölkopf, B.: The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA. In: 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Bd. 115, 53, S. 296 - 313. 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, 22. Juli 2019 - 25. Juli 2019. (2019)

Besserve, M.; Sun, R.; Schölkopf, B.: Intrinsic disentanglement: an invariance view for deep generative models. In: ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models. ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden, 14. Juli 2018 - 15. Juli 2018. (2018)

Loktyushin, A.; Ehses, P.; Schölkopf, B.; Scheffler, K.: Learning-based solution to phase error correction in T2*-weighted GRE scans. In: International Conference on Medical Imaging with Deep Learning (MIDL 2018), S. 1 - 3. International Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherlands, 04. Juli 2018 - 06. Juli 2018. (2018)

Besserve, M.; Shajarisales, N.; Schölkopf, B.; Janzing, D.: Group invariance principles for causal generative models. In: International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, S. 557 - 565 (Hg. Storkey , A.; Perez-Cruz, F.). 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Playa Blanca, Spain, 09. April 2018 - 11. April 2018. International Machine Learning Society, Madison, WI, USA (2018)

Loktyushin, A.; Schuler, C.; Scheffler, K.; Schölkopf, B.: Retrospective Motion Correction of Magnitude-Input MR Images. In: Machine Learning Meets Medical Imaging, S. 3 - 12 (Hg. Bhatia , K.K.). First International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), held in conjunction with ICML 2015, Lille, France, 11. Juli 2015. IEEE, Piscataway, NJ, USA (2016)

Shajarisales, N.; Janzing, D.; Schölkopf, B.; Besserve, M.: Telling Cause from Effect in Deterministic Linear Dynamical Systems. In: International Conference on Machine Learning, 7-9 July 2015, Lille, France, S. 285 - 294 (Hg. Bach , F.; Blei, D.). 32nd International Conference on Machine Learning (ICML 2015), Lille, France. International Machine Learning Society, Madison, WI, USA (2015)