novel causal inference methods and their foundation
physics of causality and information flow
notions of complexity and their application in machine learning
statistical physics, in particular the link between causality and the second law of thermodynamics. I founded the group "causal inference" together with Bernhard Schölkopf. The website can be found here
I have been working on quantum information theory for many years and I'm still interested in it; my current causality research is strongly influenced by the paradigm that information is physical. To see the publications from my previous field visit the following website
Dominik Janzing studied physics in Tübingen (Germany) and Cork (Ireland) and received a Ph.D. in mathematics from the Unversity of Tübingen in 1998. From 1998-2006 he was a postdoc and senior scientist at the Computer Science department of the University of Karlsruhe (TH) where he worked on quantum thermodynamics, quantum control, as well as quantum complexity theory and its physical foundations. In 2006 Since 2007 he has been working as a senior scientist at the Max Planck Institute for Biological Cybernetics in Tübingen, where he founded the group causal inference together with Bernhard Schölkopf.
The group develops novel methods for causal reasoning from statistical data. These novel approaches use complexity of conditional probability distributions for causal reasoning. The idea is strongly influenced by his previous work on complexity of physical processes and the thermodynamics of information flow.
Ried, K.Agnew, M.Vermeyden, L.Janzing, D.Spekkens, R. W.Resch, K. J.
Nature Physics, 11(5):414-420, March 2015 (Article)
The problem of inferring causal relations from observed correlations is relevant to a wide variety of scientific disciplines. Yet given the correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both. Only a randomized trial can settle the issue. Here we consider the problem of causal inference for quantum variables. We show that the analogue of a randomized trial, causal tomography, yields a complete solution. We also show that, in contrast to the classical case, one can sometimes infer the causal structure from observations alone. We implement a quantum-optical experiment wherein we control the causal relation between two optical modes, and two measurement schemes—with and without randomization—that extract this relation from the observed correlations. Our results show that entanglement and quantum coherence provide an advantage for causal inference.
In Proceedings of The 32nd International Conference on Machine Learning, 37, pages: 2218–2226, JMLR Workshop and Conference Proceedings, (Editors: Bach, F. and Blei, D.), JMLR, ICML, 2015 (Conference Paper)
In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 1917–1925, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (Conference Paper)
In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 285–294, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (Conference Paper)
In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (Conference Paper)
In Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence, pages: 440-448, (Editors: A Nicholson and P Smyth), AUAI Press, Corvallis, Oregon, UAI, 2013 (Conference Paper)
In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 556-565, (Editors: A Nicholson and P Smyth), AUAI Press Corvallis, Oregon, USA, UAI, 2013 (Conference Paper)
In Advances in Neural Information Processing Systems 26, pages: 154-162, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (Conference Paper)
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems