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Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory.
On the perturbation of pseudo-inverses, projections and linear least squares problems
GW Stewart · 1977
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Statistical analysis of finite mixture distributions , volume 7
D Michael Titterington, Adrian FM Smith, Udi E Makov, et al · 1985
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Matrix perturbation theory
Gilbert W Stewart and Ji-guang Sun · 1990
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Decoupling inequalities for the tail probabilities of multivariate u-statistics
Victor H de la Peña and Stephen J Montgomery-Smith · 1995
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Robust text-independent speaker identification using gaussian mixture speaker models
Douglas A Reynolds and Richard C Rose · 1995
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Learning mixtures of gaussians
Sanjoy Dasgupta · 1999
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A two-round variant of em for gaussian mixtures
Sanjoy Dasgupta and Leonard J Schulman · 2000
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Learning mixtures of arbitrary gaussians
Arora Sanjeev and Ravi Kannan · 2001
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Gaussian mixture models of texture and colour for image database retrieval
H Permuter, J Francos, and H Jermyn · 2003
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Finite mixture models
Geoffrey McLachlan and David Peel · 2004
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Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Daniel A Spielman and Shang-Hua Teng · 2004
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A spectral algorithm for learning mixture models
Santosh Vempala and Grant Wang · 2004
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Pac learning axis-aligned mixtures of gaussians with no separation assumption
Jon Feldman, Rocco A Servedio, and Ryan O’Donnell · 2006
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Estimates of moments and tails of gaussian chaoses
Rafał Latała et al · 2006
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On random ± \pm 1 matrices: singularity and determinant
Terence Tao and Van Vu · 2006
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Isotropic pca and affine-invariant clustering
S Charles Brubaker and Santosh S Vempala · 2008
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Learning gaussian mixtures with arbitrary separation
Mikhail Belkin and Kaushik Sinha · 2009
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Learning and smoothed analysis
Adam Tauman Kalai, Alex Samorodnitsky, and Shang-Hua Teng · 2009
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Algorithmic approaches to statistical questions
Gregory John Valiant · 2012
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Natural images, gaussian mixtures and dead leaves
Daniel Zoran and Yair Weiss · 2012
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The more, the merrier: the blessing of dimensionality for learning large gaussian mixtures
Joseph Anderson, Mikhail Belkin, Navin Goyal, Luis Rademacher, and James Voss · 2013
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Learning mixtures of spherical gaussians: moment methods and spectral decompositions
Daniel Hsu and Sham M Kakade · 2013
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Low-rank matrix completion using alternating minimization
Prateek Jain, Praneeth Netrapalli, and Sujay Sanghavi · 2013
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Smallest singular value of a random rectangular matrix
Mark Rudelson and Roman Vershynin · 2009
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Polynomial learning of distribution families
Mikhail Belkin and Kaushik Sinha · 2010
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Efficiently learning mixtures of two gaussians
Adam Tauman Kalai, Ankur Moitra, and Gregory Valiant · 2010
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Settling the polynomial learnability of mixtures of gaussians
Ankur Moitra and Gregory Valiant · 2010
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Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization
Benjamin Recht, Maryam Fazel, and Pablo A Parrilo · 2010
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Computational limits for matrix completion
Moritz Hardt, Raghu Meka, Prasad Raghavendra, and Benjamin Weitz
Cited in the paper.
Van Vu and Ke Wang · 2013
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Tensor decompositions for learning latent variable models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, and Matus Telgarsky · 2014
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Smoothed analysis of tensor decompositions
Aditya Bhaskara, Moses Charikar, Ankur Moitra, and Aravindan Vijayaraghavan · 2014
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Efficient density estimation via piecewise polynomial approximation
Siu-On Chan, Ilias Diakonikolas, Rocco A. Servedio, and Xiaorui Sun · 2014
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Computational limits for matrix completion
Moritz Hardt, Raghu Meka, Prasad Raghavendra, and Benjamin Weitz · 2014
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Learning mixtures of discrete product distributions using spectral decompositions
Prateek Jain and Sewoong Oh · 2014
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