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Towards Causal Representation Learning

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Towards Causal Representation Learning


Francesco Locatello


The two fields of machine learning and graphical causality arose and developed separately. However, there is now strong cross-pollination, and increasing interest in both fields to benefit from the advances of the other. In this talk, I will discuss my late PhD work, highlighting some points of contact between causality and machine learning, and proposing key research questions at the intersection of both. As most work in causality starts from the premise that the causal variables are observed, a central problem for AI and causality is causal representation learning: the discovery of high-level causal variables from low-level observations.


Francesco Locatello recently joined Amazon as a Senior Applied Scientist. He defended his PhD at ETH Zurich, where he was a Doctoral Fellow at the Max Planck ETH Center for Learning Systems and ELLIS supervised by Gunnar Rätsch (ETH Zurich) and Bernhard Schölkopf (Max Planck Institute for Intelligent Systems). He held a Google PhD Fellowship in Machine Learning and received the best paper award at the International Conference of Machine Learning (ICML) 2019.


2021-02-02 at 3:00 pm