Provable Representation Learning

Abstract: Representation learning has been widely used in many applications. In this talk, I will present our work, which uncovers when and why representation learning provably improves the sample efficiency, from a statistical learning point of view. I will show 1) the existence of a good representation among all tasks, and 2) the diversity of tasks are key conditions that permit improved statistical efficiency via multi-task representation learning. These conditions provably improve the sample efficiency for functions with certain complexity measures as the representation. If time permits, I will also talk about leveraging the theoretical insights to improve practical performance.

data structures and algorithmsmachine learningmathematical physicsinformation theoryoptimization and controldata analysis, statistics and probability

Audience: researchers in the topic

( video )

Mathematics, Physics and Machine Learning (IST, Lisbon)

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Zoom link: videoconf-colibri.zoom.us/j/91599759679