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We provide a novel -- and to the best of our knowledge, the first -- algorithm for high dimensional sparse regression with constant fraction of corruptions in explanatory and/or response variables.
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Peter J Huber · 1964
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John W Tukey · 1975
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The densest hemisphere problem
David S Johnson and Franco P Preparata · 1978
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Estimation of the mean of a multivariate normal distribution
Charles M Stein · 1981
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Peter J Rousseeuw and Annick M Leroy · 2005
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Frank R Hampel, Elvezio M Ronchetti, Peter J Rousseeuw, and Werner A Stahel · 2011
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Afonso S. Bandeira, Edgar Dobriban, Dustin G. Mixon, and William F. Sawin · 2013
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Yudong Chen, Constantine Caramanis, and Shie Mannor · 2013
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Nam H Nguyen and Trac D Tran · 2013
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Fantope projection and selection: A near-optimal convex relaxation of sparse PCA
Vincent Q Vu, Juhee Cho, Jing Lei, and Karl Rohe · 2013
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Huan Xu, Constantine Caramanis, and Shie Mannor · 2013
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Kush Bhatia, Prateek Jain, and Purushottam Kar · 2017
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Being robust (in high dimensions) can be practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2017
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Statistical query lower bounds for robust estimation of high-dimensional gaussians and gaussian mixtures
Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart · 2017
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Robust regression via mutivariate regression depth
Chao Gao · 2017
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Lower bounds on the performance of polynomial-time algorithms for sparse linear regression
Yuchen Zhang, Martin J Wainwright, and Michael I Jordan · 2014
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