Uncertainty Quantification for Nonconvex Tensor Completion: Confidence Intervals, Heteroscedasticity and Optimality-Reference-Cited by-同舟云学术

Uncertainty Quantification for Nonconvex Tensor Completion: Confidence Intervals, Heteroscedasticity and Optimality

Author:

Cai Changxiao¹,Poor H. Vincent²^ORCID,Chen Yuxin³^ORCID

Affiliation:

1. Department of Biostatistics, University of Pennsylvania, Philadelphia, PA, USA

2. Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA

3. Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA

Funder

Gordon Y. S. Wu Fellowship in Engineering

National Science Foundation

Princeton Schmidt Data-X Research Award

Alfred P. Sloan Research Fellowship

Air Force Office of Scientific Research (AFOSR) Young Investigator Program

Office of Naval Research

Army Research Office (ARO) YIP

NSF

Princeton School of Engineering and Applied Science (SEAS) Innovation Award

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Link

Reference96 articles.

4. A non-asymptotic framework for approximate message passing in spiked models;li;arXiv 2208 03313,2022

5. A new convex relaxation for tensor completion;romera-paredes;Proc Adv Neural Inf Process Syst,2013

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