Universal Prediction Band via Semi-Definite Programming

 0 Người đánh giá. Xếp hạng trung bình 0

Tác giả: Tengyuan Liang

Ngôn ngữ: eng

Ký hiệu phân loại: 519.7 Programming

Thông tin xuất bản: 2021

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 166645

Comment: 21 pages, 4 figuresWe 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.
Tạo bộ sưu tập với mã QR

THƯ VIỆN - TRƯỜNG ĐẠI HỌC CÔNG NGHỆ TP.HCM

ĐT: (028) 36225755 | Email: tt.thuvien@hutech.edu.vn

Copyright @2024 THƯ VIỆN HUTECH