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We study the problem of list-decodable Gaussian mean estimation and the related problem of learning mixtures of separated spherical Gaussians.
A survey of sampling from contaminated distributions
J.W. Tukey · 1960
Earlier work this paper cites.
Robust estimation of a location parameter
P. J. Huber · 1964
Earlier work this paper cites.
Mathematics and picturing of data
J.W. Tukey · 1975
Earlier work this paper cites.
Robust statistics. The approach based on influence functions
F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel · 1986
Earlier work this paper cites.
Gaussian Hilbert Spaces
S. Janson · 1997
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
M. Kearns · 1998
Earlier work this paper cites.
Learning mixtures of Gaussians
S. Dasgupta · 1999
Earlier work this paper cites.
Learning mixtures of arbitrary Gaussians
S. Arora and R. Kannan · 2001
Earlier work this paper cites.
Combinatorial methods in density estimation
L. Devroye and G. Lugosi · 2001
Earlier work this paper cites.
A spectral algorithm for learning mixtures of distributions
S. Vempala and G. Wang · 2002
Earlier work this paper cites.
A spectral algorithm for learning mixture models
S. Vempala and G. Wang · 2004
Earlier work this paper cites.
On spectral learning of mixtures of distributions
D. Achlioptas and F. McSherry · 2005
Earlier work this paper cites.
Robust regression and outlier detection
P. J Rousseeuw and A. M Leroy · 2005
Earlier work this paper cites.
Robust estimators are hard to compute
T. Bernholt · 2006
Earlier work this paper cites.
PAC learning mixtures of Gaussians with no separation assumption
J. Feldman, R. O’Donnell, and R. Servedio · 2006
Earlier work this paper cites.
A discriminative framework for clustering via similarity functions
M.-F. Balcan, A. Blum, and S. Vempala · 2008
Earlier work this paper cites.
Isotropic PCA and Affine-Invariant Clustering
S. C. Brubaker and S. Vempala · 2008
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The spectral method for general mixture models
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Polynomial learning of distribution families
M. Belkin and K. Sinha · 2010
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Settling the polynomial learnability of mixtures of Gaussians
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Statistical algorithms and a lower bound for detecting planted cliques
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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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Avoiding imposters and delinquents: Adversarial crowdsourcing and peer prediction
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Sample-optimal density estimation in nearly-linear time
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Computationally efficient robust sparse estimation in high dimensions
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Learning from untrusted data
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Learning mixtures of spherical gaussians: moment methods and spectral decompositions
D. Hsu and S. M. Kakade · 2013
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The more, the merrier: the blessing of dimensionality for learning large gaussian mixtures
J. Anderson, M. Belkin, N. Goyal, L. Rademacher, and J. R. Voss · 2014
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Smoothed analysis of tensor decompositions
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Fourier PCA and robust tensor decomposition
N. Goyal, S. Vempala, and Y. Xiao · 2014
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Near-optimal-sample estimators for spherical gaussian mixtures
A. T. Suresh, A. Orlitsky, J. Acharya, and A. Jafarpour · 2014
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On the complexity of random satisfiability problems with planted solutions
V. Feldman, W. Perkins, and S. Vempala · 2015
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Being robust (in high dimensions) can be practical
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Learning geometric concepts with nasty noise
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Statistical query algorithms for mean vector estimation and stochastic convex optimization
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