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Wednesday, June 29 • 3:30pm - 4:30pm
Flexible and Interpretable Regression Using Convex Penalties

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We consider the problem of fitting a regression model that is both flexible and interpretable. We propose two procedures for this task: the Fused Lasso Additive Model (FLAM), which is an additive model of piecewise constant fits; and Convex Regression with Interpretable Sharp Partitions (CRISP), which extends FLAM to allow for non-additivity. Both FLAM and CRISP are the solutions to convex optimization problems that can be efficiently solved. We show that FLAM and CRISP outperform competitors, such as sparse additive models (Ravikumar et al, 2009), CART (Breiman et al, 1984), and thin plate splines (Duchon, 1977), in a range of settings. We propose unbiased estimators for the degrees of freedom of FLAM and CRISP, which allow us to characterize their complexity.

This is joint work with Ashley Petersen and Noah Simon at University of Washington.


Rob Tibshirani

Stanford University
Robert Tibshirani is a Professor in the Departments of Statistics and Health Research and Policy at Stanford University. He received a B.Math. from the University of Waterloo, an M.Sc. from the University of Toronto and a Ph.D. from Stanford University. He was a Professor at the University... Read More →

avatar for Daniela Witten

Daniela Witten

University of Washington

Wednesday June 29, 2016 3:30pm - 4:30pm PDT
McCaw Hall