Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration, and Lower Bounds

Author:

Bartan Burak1ORCID,Pilanci Mert1ORCID

Affiliation:

1. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

Funder

National Science Foundation

NSF CAREER Award

U.S. Army Research Office Early Career Award

Stanford Precourt Institute

ACCESS—Artificial Intelligence (AI) Chip Center for Emerging Smart Systems through InnoHK, Hong Kong, SAR

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Reference44 articles.

1. Utility-based resource allocation and pricing for serverless computing;gupta;arXiv 2008 07793,2020

2. Ridge regression: Structure, cross-validation, and sketching;liu;Proc 8th Int Conf Learn Represent,2020

3. A case for serverless machine learning;carreira;Proc Workshop Syst ML Open Source Softw,2018

4. Debiasing distributed second order optimization with surrogate sketching and scaled regularization;derezinski;Proc Adv Neural Inf Process Syst,2020

5. Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise

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