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We study the fundamental task of outlier-robust mean estimation for heavy-tailed distributions in the presence of sparsity.
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The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing
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Robust estimators in high dimensions without the computational intractability
I. Diakonikolas, G. Kamath, D. M. Kane, J. Li, A. Moitra, and A. Stewart · 2016
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Statistical query lower bounds for robust estimation of high-dimensional Gaussians and Gaussian mixtures
I. Diakonikolas, D. M. Kane, and A. Stewart · 2017
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Efficient algorithms for outlier-robust regression
A. Klivans, P. Kothari, and R. Meka · 2018
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Principled Approaches to Robust Machine Learning and Beyond
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Recent advances in algorithmic high-dimensional robust statistics
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Sever: A robust meta-algorithm for stochastic optimization
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Outlier-robust high-dimensional sparse estimation via iterative filtering
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A fast spectral algorithm for mean estimation with sub-gaussian rates
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Robust covariance estimation under L 4 − L 2 {L}_{4}-{L}_{2} norm equivalence
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A robust univariate mean estimator is all you need
A. Prasad, S. Balakrishnan, and P. Ravikumar · 2020
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Robust regression with covariate filtering: Heavy tails and adversarial contamination
A. Pensia, V. Jog, and P. Loh · 2020
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Robust estimation via robust gradient estimation
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