EE Seminar: How to Learn Under Constraints?
https://us02web.zoom.us/j/87990044156?pwd=bHNKTy9MSkExS3YwSDY4ajRZZElFdz09
Meeting ID: 879 9004 4156
Passcode: TAUEESYS
Speaker: Dr. Michal Moshkovitz
Qualcomm Institute at UC San Diego
Monday, November 16th, 2020, at 18:00
How to Learn Under Constraints?
Abstract
The goal of classical machine learning is to learn a high-accuracy model from given examples. The learning process has no constraints besides running in a reasonable time relative to the input size. However, these days, as machine learning is utilized in high-stake applications like healthcare and law, learning has to obey several new constraints. In this talk, I will focus on two types of constraints: explainability and bounded memory. I will present the first explainable algorithm for k-means clustering that has provable guarantees. Then I will focus on another constraint -- learning with bounded memory, where I will present a characterization of high-accuracy learning with bounded memory and its equivalence to learning with statistical queries.
Short Bio
Michal is a postdoctoral fellow at the Qualcomm Institute of the University of California, San Diego. Her interests lie in the foundations of AI, exploring how different constraints affect learning. She works on explainable machine learning, bounded memory learning, and online no-substitution clustering. Michal received her PhD from the Hebrew University and an MSc from Tel-Aviv University. She was the recipient of an Anita Borg scholarship from Google and a Hoffman scholarship from the Hebrew University.
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