Tengyuan Liang, University of Chicago

Universal Prediction Band and Variance Interpolation via Semi-Definite Programming
Nov 28, 2022, 12:30 pm1:30 pm
101 - Sherrerd Hall
Event Description

Abstract: We propose a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification, with or without any user-specified predictive model. Our approach provides an alternative to the now-standard conformal prediction for uncertainty quantification, with novel theoretical insights and computational advantages. The data-adaptive prediction band is universally applicable with minimal distributional assumptions, has strong non-asymptotic coverage properties, and is easy to implement using standard convex programs. Our approach can be viewed as a novel variance interpolation with confidence and further leverages techniques from semi-definite programming and sum-of-squares optimization. Theoretical and numerical performances for the proposed approach for uncertainty quantification are analyzed.

Event Category
S. S. Wilks Memorial Seminar in Statistics