Seminar

Universal Prediction Band via Semi-Definite Programming

Speaker

Tengyuan Liang (Chicago Booth)

Date & Time

From: 24 May 2022, 17:00
Until: 24 May 2022, 18:00

Type

Seminar

Venue

Online only

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.