Publikationen von D Janzing

Peters, J.; Janzing, D.; Schölkopf, B.: Causal Inference on Discrete Data using Additive Noise Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (12), S. 2436 - 2450 (2011)

Janzing, D.; Schölkopf, B.: Causal Inference Using the Algorithmic Markov Condition. IEEE Transactions on Information Theory 56 (10), S. 5168 - 5194 (2010)

Janzing, D.; Steudel, B.: Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory. Open Systems and Information Dynamics 17 (2), S. 189 - 212 (2010)

Janzing, D.: On the Entropy Production of Time Series with Unidirectional Linearity. Journal of Statistical Physics 138 (4-5), S. 767 - 779 (2010)

Janzing, D.; Wocjan, P.: A promise BQP-complete string rewriting problem. Quantum Information and Computation 10 (3), S. 234 - 257 (2010)

Allahverdyan, A.; Janzing, D.; Mahler, G.: Thermodynamic efficiency of information and heat flow. Journal of Statistical Mechanics: Theory and Experiment 2009 (9), P09011, S. 1 - 35 (2009)

Janzing, D.; Wocjan, P.; Zhang, S.: A Single-shot Measurement of the Energy of Product States in a Translation Invariant Spin Chain Can Replace Any Quantum Computation. New Journal of Physics 10 (8), 093004, S. 1 - 18 (2008)

Sun, X.; Janzing, D.; Schölkopf, B.: Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods. Neurocomputing 71 (7-9), S. 1248 - 1256 (2008)

Causality: Objectives and Assessment. NIPS 2008 Workshop: Causality: Objectives and Assessment , Whistler, BC, Canada, 12. Dezember 2008. (2010)

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)

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)

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)

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)

Besserve, M.; Janzing, D.; Logothetis, N.; Schölkopf, B.: Finding dependencies between frequencies with the kernel cross-spectral density. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), S. 2080 - 2083. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), Praha, Czech Republic, 22. Mai 2011 - 27. Mai 2011. IEEE, Piscataway, NJ, USA (2011)

Janzing, D.; Sgouritsa, E.; Stegle, O.; Peters, J.; Schölkopf, B.: Detecting low-complexity unobserved causes. In: 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), S. 383 - 391 (Hg. Cozman, F.; Pfeffer, A.). 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain, 14. Juli 2011 - 17. Juli 2011. 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, 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)

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)

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)

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)