Talk "Causality-inspired machine learning" by Uri Shalit, Technion, Haifa
We are happy to announce the following talk in the ETH Foundations of Data Science Seminar
Time: Wednesday, 22. January 2020, 13h15
Place: ETH Zurich HG E 1.1
We will present three recent projects where ideas from causal inference have inspired us to find new approaches to problems in machine learning. First, we will see how the idea of negative controls led us to find a way to perform off-policy evaluation in a partially observable Markov decision process (POMDP).
We will then present how using the idea of independence of cause and mechanism (ICM) can be used to help learn predictive models that are stable against a-priori unknown distributional shifts. Finally, we will see how thinking in terms of causal graphs led us to a new method for learning computer vision models that can better generalize to unseen object-attribute compositions in images.
Organisers: A. Bandeira, H. Bölcskei, P. Bühlmann, J. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.-A. Loeliger, M. Maathuis, N. Meinshausen, G. Rätsch, C. Uhler, S. van de Geer, F. Yang
Seminar website: https://math.ethz.ch/sfs/news-and-events/data-science-seminar.html