Seminar: Machine Learning Seminar

Learning invariant models for out-of-domain generalization: good news and bad news

Date: February,14,2024 Start Time: 10:30 - 11:30
Location: 1061, Meyer Building
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Lecturer: Uri Shalit
I will present work showing how a robust notion of model calibration ties into the idea of invariant learning, and leads to models that can generalize out-of-domain (OOD) in both theory and practice. I will then show how practical difficulties with optimizing the above models lead us to a new result with ramifications for OOD generalization, fairness and robustness: We prove how “benign overfitting”, where deep models interpolate the training set yet generalize well, can be fundamentally at odds with learning invariant models.
Uri Shalit is an associate professor at the Technion – Israel Institute of Technology. Previously, Uri was a postdoctoral researcher in Prof. David Sontag’s Clinical Machine Learning Lab at NYU and then MIT.


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