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foundation models
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S&DS Seminar: Jingfeng Wu (Berkeley), “Gradient Descent Dominates Ridge: A Statistical View on Implicit Regularization”
Talk summary: A key puzzle in deep learning is how simple gradient methods find generalizable solutions without explicit regularization. This talk discusses the implicit […]
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S&DS Seminar: Alex Damian (Princeton), “Learning From Gaussian Data: Single and Multi-Index Models”
Abstract: In this work we consider generic Gaussian Multi-index models, in which the labels only depend on the (Gaussian) d-dimensional inputs through their projection onto […]
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Lu & Tassiulas awarded DOE funding for artificial intelligence research
The Lu Group (Professor Lu Lu, Department of Statistics and Data Science and member of FDS) has been awarded a new $4 Million grant from […]
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FDS Colloquium: Quanquan C. Liu, “Massive Graph Algorithms from Theory to Practice and Back”
“Massive Graph Algorithms from Theory to Practice and Back” Abstract: In the face of massive graph data, there is increased interest in developing novel […]
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