Projects
Reading
People
Chat
SU\G
(𝔸)
/K·U
Projects
Reading
People
Chat
Sign Up
Light
Dark
System
Statistical properties of sketching algorithms
Daniel Ahfock
,
William J. Astle
,
Sylvia Richardson
Type:
Preprint
Publication Date:
2017-06-12
Citations:
21
View
Share
Locations
arXiv (Cornell University) -
View
Similar Works
Action
Title
Year
Authors
+
Statistical properties of sketching algorithms
2017
Daniel Ahfock
William J. Astle
Sylvia Richardson
+
PDF
Chat
Statistical properties of sketching algorithms
2020
Daniel Ahfock
William J. Astle
Sylvia Richardson
+
A New Theory for Sketching in Linear Regression.
2018
Edgar Dobriban
Sifan Liu
+
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares
2014
Garvesh Raskutti
Michael W. Mahoney
+
Statistical inference for sketching algorithms
2023
Ryan P. Browne
Jeffrey L. Andrews
+
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs
2022
Michał Dereziński
+
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares
2014
Garvesh Raskutti
Michael W. Mahoney
+
Statistical inference for sketching algorithms
2024
Ryan P. Browne
Jeffrey L. Andrews
+
A Framework for Statistical Inference via Randomized Algorithms
2023
Zhi-Xiang Zhang
Sokbae Lee
Edgar Dobriban
+
Asymptotics for Sketching in Least Squares Regression
2018
Edgar Dobriban
Sifan Liu
+
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares
2015
Garvesh Raskutti
Michael W. Mahoney
+
Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares -- ICML
2015
Garvesh Raskutti
Michael W. Mahoney
+
On randomized sketching algorithms and the Tracy-Widom law
2022
Daniel Ahfock
William J. Astle
Sylvia Richardson
+
PDF
Chat
On randomized sketching algorithms and the Tracy–Widom law
2023
Daniel Ahfock
William J. Astle
Sylvia Richardson
+
PDF
Chat
Inference in Randomized Least Squares and PCA via Normality of Quadratic Forms
2024
L. S. Wang
Zhi-Xiang Zhang
Edgar Dobriban
+
Distributed Sketching Methods for Privacy Preserving Regression
2020
Burak Bartan
Mert Pilancı
+
Precise expressions for random projections: Low-rank approximation and randomized Newton
2020
Michał Dereziński
Feynman Liang
Zhenyu Liao
Michael W. Mahoney
+
Precise expressions for random projections: Low-rank approximation and randomized Newton
2020
Michał Dereziński
Feynman Liang
Zhenyu Liao
Michael W. Mahoney
+
Density Sketches for Sampling and Estimation
2021
Aditya Desai
Benjamin Coleman
Anshumali Shrivastava
+
PDF
Chat
Estimation de mélange de Gaussiennes sur données compressées
2013
Anthony Bourrier
Rémi Gribonval
Patrick Pérez
Cited by (20)
Action
Title
Year
Authors
+
PDF
Chat
Lower Bounds and a Near-Optimal Shrinkage Estimator for Least Squares Using Random Projections
2020
Srivatsan Sridhar
Mert Pilancı
Ayfer Özgür
+
Sketching in Bayesian High Dimensional Regression With Big Data Using Gaussian Scale Mixture Priors
2021
Rajarshi Guhaniyogi
Aaron Scheffler
+
PDF
Chat
Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD)
2021
Salman Ahmadi‐Asl
Stanislav Abukhovich
Maame G. Asante-Mensah
Andrzej Cichocki
Anh Huy Phan
Toshihisa Tanaka
Ivan Oseledets
+
Automated Scalable Bayesian Inference via Hilbert Coresets
2017
Trevor Campbell
Tamara Broderick
+
Sparse Variational Inference: Bayesian Coresets from Scratch
2019
Trevor Campbell
Boyan Beronov
+
PDF
Chat
Random projections: Data perturbation for classification problems
2020
Timothy I. Cannings
+
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
2018
Miles E. Lopes
Shusen Wang
Michael W. Mahoney
+
Ridge Regression: Structure, Cross-Validation, and Sketching
2019
Sifan Liu
Edgar Dobriban
+
PDF
Chat
An econometric perspective on algorithmic subsampling
2020
Serena Ng
Sokbae Lee
+
Error Estimation for Sketched SVD via the Bootstrap
2020
Miles E. Lopes
N. Benjamin Erichson
Michael W. Mahoney
+
Sketching for Two-Stage Least Squares Estimation.
2020
Sokbae Lee
Serena Ng
+
An Econometric Perspective on Algorithmic Subsampling
2019
Sokbae Lee
Serena Ng
+
PDF
Chat
Randomized Matrix Decompositions Using <i>R</i>
2019
N. Benjamin Erichson
Sergey Voronin
Steven L. Brunton
J. Nathan Kutz
+
Lower Bounds and a Near-Optimal Shrinkage Estimator for Least Squares using Random Projections
2020
Srivatsan Sridhar
Mert Pilancı
Ayfer Özgür
+
On Principal Components Regression, Random Projections, and Column Subsampling
2017
Martin Slawski
+
Randomized Algorithms for Computation of Tucker decomposition and Higher Order SVD (HOSVD)
2020
Salman Ahmadi‐Asl
Stanislav Abukhovich
Maame G. Asante-Mensah
Andrzej Cichocki
Anh Huy Phan
Toshihisa Tanaka
Ivan Oseledets
+
A New Theory for Sketching in Linear Regression.
2018
Edgar Dobriban
Sifan Liu
+
Asymptotics for Sketching in Least Squares Regression
2018
Edgar Dobriban
Sifan Liu
+
Matrix sketching for supervised classification with imbalanced classes
2019
Roberta Falcone
Angela Montanari
Laura Anderlucci
+
PDF
Chat
Robust Inference after Random Projections via Hellinger Distance for Location-Scale Family
2019
Lei Li
Anand N. Vidyashankar
Guoqing Diao
Ejaz Ahmed
Citing (28)
Action
Title
Year
Authors
+
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares
2014
Garvesh Raskutti
Michael W. Mahoney
+
Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments
2015
Jiyan Yang
Xiangrui Meng
Michael W. Mahoney
+
Multivariate Statistics: A Vector Space Approach.
1985
Leon Jay Gleser
Morris L. Eaton
+
PDF
Chat
Bayesian Compressed Regression
2014
Rajarshi Guhaniyogi
David B. Dunson
+
PDF
Chat
Randomized Algorithms for Matrices and Data
2012
Michael W. Mahoney
+
On the central limit theorem for negatively correlated random variables with negatively correlated squares
2000
Alexander R. Pruss
Dominik Szynal
+
PDF
Chat
Efficient Gaussian process regression for large datasets
2012
Anjishnu Banerjee
David B. Dunson
Surya T. Tokdar
+
Sampling algorithms for <i>l</i><sub>2</sub> regression and applications
2006
Petros Drineas
Michael W. Mahoney
S. Muthukrishnan
+
Leveraging for big data regression
2014
Ping Ma
Xiaoxiao Sun
+
PDF
Chat
Low rank approximation and regression in input sparsity time
2013
Kenneth L. Clarkson
David P. Woodruff
+
PDF
Chat
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
2011
Nathan Halko
Per‐Gunnar Martinsson
Joel A. Tropp
+
PDF
Chat
Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
2013
Xiangrui Meng
Michael W. Mahoney
+
All Invariant Moments of the Wishart Distribution
2004
Gérard Letac
Hélène Massam
+
Min-wise hashing for large-scale regression and classication with sparse data
2013
Rajen D. Shah
Nicolai Meinshausen
+
PDF
Chat
Random projections for Bayesian regression
2015
Leo N. Geppert
Katja Ickstadt
Alexander Munteanu
Jens Quedenfeld
Christian Sohler
+
Coresets and Sketches
2016
Jeff M. Phillips
+
Sub-Sampled Newton Methods I: Globally Convergent Algorithms
2016
Farbod Roosta-Khorasani
Michael W. Mahoney
+
PDF
Chat
Random Projections for Large-Scale Regression
2017
Gian-Andrea Thanei
Christina Heinze
Nicolai Meinshausen
+
A Statistical Perspective on Algorithmic Leveraging
2013
Ping Ma
Michael W. Mahoney
Bin Yu
+
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
2018
Miles E. Lopes
Shusen Wang
Michael W. Mahoney
+
PDF
Chat
Convergence of Probability Measures
1999
Patrick Billingsley
+
Randomized Least Squares Regression: Combining Model- and Algorithm-Induced Uncertainties.
2018
T. Jocelyn
Ilse C. F. Ipsen
+
A New Theory for Sketching in Linear Regression.
2018
Edgar Dobriban
Sifan Liu
+
Random-projection ensemble classification
2015
Timothy I. Cannings
Richard J. Samworth
+
Sampling algorithms for l2 regression and applications
2006
Petros Drineas
Michael W. Mahoney
S. Muthukrishnan
+
Wiley Series in Probability and Statistics
2007
+
PDF
Chat
Probability for Statisticians
2017
Galen R. Shorack
+
PDF
Chat
Randomized Iterative Methods for Linear Systems
2015
Robert M. Gower
Peter Richtárik