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Events by date

Friday, October 9, 2020

Friday, October 9, 2020 — 3:30 PM EDT

Title: Generalization bounds for rational self-supervised learning algorithms

Speaker: Boaz Barak Affiliation: Harvard University Zoom: Please email Emma Watson

Abstract:

The generalization gap of a learning algorithm is the expected difference between its performance on the training data and its performance on fresh unseen test samples.Modern deep learning algorithms typically have large generalization gaps, as they use more parameters than the size of their training set. Moreover the best known rigorous bounds on their generalization gap are often vacuous.

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