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We study the problem of robust linear regression with response variable corruptions.
A general qualitative definition of robustness
Frank R Hampel · 1971
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Robust regression: asymptotics, conjectures and monte carlo
Peter J Huber et al · 1973
Earlier work this paper cites.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
Earlier work this paper cites.
Least median of squares regression
Peter J Rousseeuw · 1984
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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An elementary proof of a theorem of Johnson and Lindenstrauss
Sanjoy Dasgupta and Anupam Gupta · 2003
Earlier work this paper cites.
Univariate Discrete Distributions , volume 444
Norman L Johnson, Adrienne W Kemp, and Samuel Kotz · 2005
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Matlab code by Mark Schmidt, 2006
Mark Schmidt · 2006
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Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ 1 \ell_{1} -constrained quadratic programming (lasso)
Martin J Wainwright · 2009
Earlier work this paper cites.
Restricted eigenvalue conditions on subgaussian random matrices
Shuheng Zhou · 2009
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Fast global convergence rates of gradient methods for high-dimensional statistical recovery
Alekh Agarwal, Sahand Negahban, and Martin J Wainwright · 2010
Earlier work this paper cites.
Restricted eigenvalue properties for correlated gaussian designs
Garvesh Raskutti, Martin J Wainwright, and Bin Yu · 2010
Earlier work this paper cites.
Dense error correction via ℓ 1 \ell_{1} -minimization
John Wright and Yi Ma · 2010
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Robust lasso with missing and grossly corrupted observations
Nasser M Nasrabadi, Trac D Tran, and Nam Nguyen · 2011
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Variance estimation using refitted cross-validation in ultrahigh dimensional regression
Jianqing Fan, Shaojun Guo, and Ning Hao · 2012
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A tail inequality for quadratic forms of sub-Gaussian random vectors
Daniel Hsu, Sham Kakade, and Tong Zhang · 2012
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Recovery of sparsely corrupted signals
Christoph Studer, Patrick Kuppinger, Graeme Pope, and Helmut Bolcskei · 2012
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Scaled sparse linear regression
Tingni Sun and Cun-Hui Zhang · 2012
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How close is the sample covariance matrix to the actual covariance matrix?
Roman Vershynin · 2012
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Sub-Gaussian mean estimators
Luc Devroye, Matthieu Lerasle, Gabor Lugosi, Roberto I Oliveira, et al · 2016
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Robust estimators in high dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2016
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Loss minimization and parameter estimation with heavy tails
Daniel Hsu and Sivan Sabato · 2016
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Agnostic estimation of mean and covariance
Kevin A Lai, Anup B Rao, and Santosh Vempala · 2016
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Consistent robust regression
Kush Bhatia, Prateek Jain, Parameswaran Kamalaruban, and Purushottam Kar · 2017
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Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions
Jianqing Fan, Quefeng Li, and Yuyan Wang · 2017
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Exact recoverability from dense corrupted observations via ℓ 1 \ell_{1} -minimization
Nam H Nguyen and Trac D Tran · 2013
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Fast ℓ 1 \ell_{1} -minimization algorithms for robust face recognition
Allen Y Yang, Zihan Zhou, Arvind Ganesh Balasubramanian, S Shankar Sastry, and Yi Ma · 2013
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Variance estimation in high-dimensional linear models
Lee H Dicker · 2014
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On iterative hard thresholding methods for high-dimensional m-estimation
Prateek Jain, Ambuj Tewari, and Purushottam Kar · 2014
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Convergence of the huber regression m-estimate in the presence of dense outliers
Efthymios Tsakonas, Joakim Jaldén, Nicholas D Sidiropoulos, and Björn Ottersten · 2014
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Robust regression via hard thresholding
Kush Bhatia, Prateek Jain, and Purushottam Kar · 2015
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Accelerated gradient descent escapes saddle points faster than gradient descent
Chi Jin, Praneeth Netrapalli, and Michael I Jordan · 2017
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Robust machine learning by median-of-means: theory and practice
Guillaume Lecué and Matthieu Lerasle · 2017
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Statistical consistency and asymptotic normality for high-dimensional robust m m -estimators
Po-Ling Loh et al · 2017
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Sever: A robust meta-algorithm for stochastic optimization
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart · 2018
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Efficient algorithms for outlier-robust regression
Adam Klivans, Pravesh K Kothari, and Raghu Meka · 2018
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Robust estimation via robust gradient estimation
Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, and Pradeep Ravikumar · 2018
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Adaptive Huber regression
Qiang Sun, Wen-Xin Zhou, and Jianqing Fan · 2018
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