Mathematics Data Science Seminar: Dr. Byol Kim -Black-box tests for algorithmic stability
This event is in the past.
Speaker: Dr. Byol Kim, Statistics department, University of Washington
Time: Wednesday, November 1, 1:30pm-2:30pm
Place: Virtual.
Zoom link:
https://wayne-edu.zoom.us/j/99751642454?pwd=cjZIM1FUU0lEdUIweHpMRTBxYmd2QT09
Title: Black-box tests for algorithmic stability
Abstract: Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e.g., removal of a single data point) may affect the outputs of a regression algorithm. Knowing an algorithm's stability properties is often useful for many downstream applications—for example, stability is known to lead to desirable generalization properties and predictive inference guarantees. However, many modern algorithms currently used in practice are too complex for a theoretical analysis of their stability properties, and thus we can only attempt to establish these properties through an empirical exploration of the algorithm's behavior on various data sets. In this work, we lay out a formal statistical framework for this kind of black-box testing without any assumptions on the algorithm or the data distribution, and establish fundamental bounds on the ability of any black-box test to identify algorithmic stability. This talk is based on a joint work with Rina Foygel Barber.