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We study the fundamental problem of learning the parameters of a high-dimensional Gaussian in the presence of noise -- where an $\varepsilon$-fraction of our samples were chosen by an adversary.
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J. W. Tukey · 1975
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Multivariate estimation with high breakdown point
P. Rousseeuw · 1985
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Robust statistics. The approach based on influence functions
F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel · 1986
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Geometric Algorithms and Combinatorial Optimization
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Adaptive estimation of a quadratic functional by model selection
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Distributional and L q L^{q} norm inequalities for polynomials over convex bodies in R n R^{n}
A. Carbery and J. Wright · 2001
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Combinatorial methods in density estimation
L. Devroye and G. Lugosi · 2001
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Robust statistics: A brief introduction and overview
F. Hampel · 2001
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D. M Blei, A. Y. Ng, and M. I. Jordan · 2003
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Robust regression and outlier detection
P. J. Rousseeuw and A. M. Leroy · 2005
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Robust estimators are hard to compute
T. Bernholt · 2006
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B. Klartag · 2007
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Genes mirror geography within europe
J. Novembre, T. Johnson, K. Bryc, Z. Kutalik, A. R. Boyko, A. Auton, A. Indap, K. S. King, S. Bergmann, M. R. Nelson, et al · 2008
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P.J. Huber and E. M. Ronchetti · 2009
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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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Robust learning of fixed-structure bayesian networks
I. Diakonikolas, D. M. Kane, and A. Stewart · 2016
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Agnostic estimation of mean and covariance
K. A. Lai, A. B. Rao, and S. Vempala · 2016
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Computationally efficient robust sparse estimation in high dimensions
S. Balakrishnan, S. S. Du, J. Li, and A. Singh · 2017
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Being robust (in high dimensions) can be practical
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