Speaker |
Jonas Peters |
|---|---|
Affiliation |
ETH Zurich |
Date |
Friday, 10 November 2023 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
We present two different works that have recently helped us when analyzing real-world data: the first paper: https://arxiv.org/abs/2203.06056 considers the problem of instrumental variables in a vector auto-regressive setting; the second paper: https://arxiv.org/abs/2306.10983 introduces effect-invariance, which can help to learn policies that generalize better between subjects. |
Biography |
Jonas is interested in using different types of data to predict the effect of interventions and to build statistical methods that are robust with respect to distributional shifts. He seeks to combine theory and methodology and tries to let real world applications guide his research. His work relates to areas such as causal inference, distribution generalization, dynamical systems, policy learning, graphical models, and independence testing. Since 2023, Jonas is professor in statistics at ETH Zurich. Previously, he has been a professor at the Department of Mathematical Sciences at the University of Copenhagen and a group leader at the Max-Planck-Institute for Intelligent Systems in Tuebingen. He studied Mathematics at the University of Heidelberg and the University of Cambridge and obtained his PhD jointly from MPI and ETH. |