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The Expectation-Maximization (EM) algorithm is a widely used method for maximum likelihood estimation in models with latent variables.
Maximum likelihood from incomplete data via the EM algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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On the convergence properties of the EM algorithm
CF Jeff Wu · 1983
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Mixture densities, maximum likelihood and the EM algorithm
Richard A Redner and Homer F Walker · 1984
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Learning mixtures of gaussians
Sanjoy Dasgupta · 1999
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Learning mixtures of arbitrary gaussians
Sanjeev Arora and Ravi Kannan · 2001
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An analysis of the EM algorithm and entropy-like proximal point methods
Paul Tseng · 2004
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A spectral algorithm for learning mixture models
Santosh Vempala and Grant Wang · 2004
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On spectral learning of mixtures of distributions
Dimitris Achlioptas and Frank McSherry · 2005
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The spectral method for general mixture models
Ravindran Kannan, Hadi Salmasian, and Santosh Vempala · 2005
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A probabilistic analysis of em for mixtures of separated, spherical gaussians
Sanjoy Dasgupta and Leonard Schulman · 2007
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Isotropic PCA and affine-invariant clustering
S Charles Brubaker and Santosh S Vempala · 2008
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On EM algorithms and their proximal generalizations
Stéphane Chrétien and Alfred O Hero · 2008
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Learning Mixtures of Product Distributions Using Correlations and Independence
Kamalika Chaudhuri and Satish Rao · 2008
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Learning mixtures of gaussians using the k-means algorithm
Kamalika Chaudhuri, Sanjoy Dasgupta, and Andrea Vattani · 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
Faster and sample near-optimal algorithms for proper learning mixtures of gaussians
Constantinos Daskalakis and Gautam Kamath · 2014
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Near-optimal-sample estimators for spherical gaussian mixtures
Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, and Ashkan Jafarpour · 2014
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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On the structure, covering, and learning of poisson multinomial distributions
Constantinos Daskalakis, Gautam Kamath, and Christos Tzamos · 2015
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Learning mixtures of gaussians in high dimensions
Rong Ge, Qingqing Huang, and Sham M Kakade · 2015
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Tight bounds for learning a mixture of two gaussians
Moritz Hardt and Eric Price · 2015
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Settling the polynomial learnability of mixtures of gaussians
Ankur Moitra and Gregory Valiant · 2010
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Combinatorial methods in density estimation
Luc Devroye and Gábor Lugosi · 2012
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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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High-dimensional statistics: A non-asymptotic viewpoint
JM Wainwright · 2015
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Global analysis of Expectation Maximization for mixtures of two Gaussians
Ji Xu, Daniel Hsu, and Arian Maleki · 2016
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Statistical guarantees for the em algorithm: From population to sample-based analysis
Sivaraman Balakrishnan, Martin J Wainwright, and Bin Yu · 2017
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