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A Projector-Based Approach to Quantifying Total and Excess Uncertainties
  for Sketched Linear Regression
v1v2v3 (latest)

A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression

17 August 2018
Jocelyn T. Chi
Ilse C. F. Ipsen
ArXiv (abs)PDFHTML

Papers citing "A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression"

3 / 3 papers shown
Title
Recent and Upcoming Developments in Randomized Numerical Linear Algebra
  for Machine Learning
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
Michał Dereziński
Michael W. Mahoney
81
11
0
17 Jun 2024
An Econometric Perspective on Algorithmic Subsampling
An Econometric Perspective on Algorithmic Subsampling
Serena Ng
S. Lee
57
13
0
03 Jul 2019
Statistical properties of sketching algorithms
Statistical properties of sketching algorithms
Daniel Ahfock
W. Astle
S. Richardson
92
39
0
12 Jun 2017
1