Theoretical Machine Learning Seminar

Margins, perceptrons, and deep networks

This talk surveys the role of margins in the analysis of deep networks. As a concrete highlight, it sketches a perceptron-based analysis establishing that shallow ReLU networks can achieve small test error even when they are quite narrow, sometimes even logarithmic in the sample size and inverse target error. The analysis and bounds depend on a certain nonlinear margin quantity due to Nitanda and Suzuki, and can lead to tight upper and lower sample complexity bounds. Joint work with Ziwei Ji.

Date & Time

March 26, 2020 | 12:00pm – 1:30pm

Location

https://illinois.zoom.us/j/741628827

Speakers

Matus Telgarsky

Affiliation

University of Illinois