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We study the problem of Robust Least Squares Regression (RLSR) where several response variables can be adversarially corrupted.
On the Method of Least Squares
Adrien-Marie Legendre (1805) · 1959
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Least Median of Squares Regression
Peter J. Rousseeuw · 1984
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Robust Regression and Outlier Detection
Peter J. Rousseeuw and Annick M. Leroy · 1987
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Adaptive estimation of a quadratic functional by model selection
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Computing LTS Regression for Large Data Sets
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A Review of Fast ℓ 1 \ell_{1} -Minimization Algorithms for Robust Face Recognition
Allen Y. Yang, Arvind Ganesh, Zihan Zhou, Shankar Sastry, and Yi Ma · 2012
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Robust Sparse Regression under Adversarial Corruption
Yudong Chen, Constantine Caramanis, and Shie Mannor · 2013
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Exact recoverability from dense corrupted observations via L1 minimization
Nam H. Nguyen and Trac D. Tran · 2013
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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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Fast and Robust Least Squares Estimation in Corrupted Linear Models
Brian McWilliams, Gabriel Krummenacher, Mario Lucic, and Joachim M. Buhmann · 2014
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