Thursday, March 2, 2023, 3pm
Causal representation learning tackles the problem of discovering high-level variables and their relations from low-level observations and aligns with the general goal of learning meaningful data representations that are also robust, explainable, and fair.
In this talk, I will discuss opportunities and challenges in discovering latent structure and causal relations from data. First, I will discuss the identifiability of causal and disentangled representations. Second, I will question whether neural networks can represent abstract causal variables and introduce Slot Attention, an architectural interface between distributed representations and sets of high-level variables. Third, I will explore how advances in machine learning enable a new generation of causal discovery algorithms. Finally, I will present my future plans for causal representation learning and, more generally, broadening the applicability of causal models in machine learning.
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Dr. Francesco Locatello is a Senior Applied Scientist at Amazon AWS, where he leads the Causal Representation Learning research team. He obtained his Ph.D. at ETH Zurich, supervised by Gunnar Rätsch (ETH Zurich) and Bernhard Schölkopf (Max Planck Institute for Intelligent Systems) in 2020. He held doctoral fellowships at the Max Planck ETH Center for Learning Systems and at ELLIS. In addition, he received the Google Ph.D. Fellowship in Machine Learning in 2019. His research has won several awards, including the best paper award at ICML 2019, the ETH medal for outstanding doctoral dissertation, and the 2023 Hector-Stiftung prize.
Faculty Host: Pradeep Ravikumar
In Person and Zoom Participation. See announcement.
Event Type: Talks
Room Number: In Person and Virtual - ET
Building: Newell-Simon 4305 and Zoom
Speaker's Name: FRANCESCO LOCATELLO
Speaker Website: www.francescolocatello.com
Speaker's Professional Title: Senior Applied Scientist, Amazon AWS
Talk Title: Causal Representation Learning
For More Information: astowers@andrew.cmu.edu
Affiliations: Machine Learning Department (MLD)
Organization(s): SCS