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H.B. Keller Colloquium Series

Monday, October 17, 2022
4:00pm to 5:00pm
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Annenberg 105
Causality for Machine Learning: Causal Approaches to Improving ML Systems
Alexander D 'Amour, Senior Research Scientist, Google Brain,

Despite their many successes, modern machine learning systems can behave in surprising ways when deployed in real-world contexts. A key challenge is that the standard ML pipeline is built on criteria that are domain- and application-agnostic, and these procedures provide few guarantees beyond aggregate performance on the data distribution that the model was trained on. In this talk, I will review some of these difficulties, and discuss approaches that leverage ideas and formalism from causal inference to control or adjust the signal that models use to make predictions. I will focus specifically on how causal formalism can be used to design robust and adaptable ML systems in the presence of "spurious" associations.

For more information, please contact Diana Bohler by phone at 16263951768 or by email at [email protected].