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We study the convergence rates of the EM algorithm for learning two-component mixed linear regression under all regimes of signal-to-noise ratio (SNR).
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Hypothesis test for normal mixture models: The EM approach
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Non-finite Fisher information and homogeneity: an EM approach
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Introduction to the non-asymptotic analysis of random matrices
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Spectral experts for estimating mixtures of linear regressions
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A convex formulation for mixed regression with two components: Minimax optimal rates
Y. Chen, X. Yi, and C. Caramanis · 2014
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X. Yi, C. Caramanis, and S. Sanghavi · 2014
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Convergence rates of parameter estimation for some weakly identifiable finite mixtures
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Global analysis of expectation maximization for mixtures of two Gaussians
J. Xu, D. Hsu, and A. Maleki · 2016
Benefits of over-parameterization with EM
J. Xu, D. J. Hsu, and A. Maleki · 2018
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Learning mixtures of linear regressions in subexponential time via fourier moments
S. Chen, J. Li, and Z. Song · 2019
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Convergence rates for Gaussian mixtures of experts
N. Ho, C.-Y. Yang, and M. I. Jordan · 2019
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List-decodable linear regression
S. Karmalkar, A. Klivans, and P. Kothari · 2019
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Global convergence of the EM algorithm for mixtures of two component linear regression
J. Kwon, W. Qian, C. Caramanis, Y. Chen, and D. Davis · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
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Statistical guarantees for the EM algorithm: From population to sample-based analysis
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Ten steps of EM suffice for mixtures of two Gaussians
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Convergence of gradient EM on multi-component mixture of Gaussians
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Theoretical guarantees for EM under misspecified Gaussian mixture models
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Strong identifiability and optimal minimax rates for finite mixture estimation
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Sharp analysis of expectation-maximization for weakly identifiable models
R. Dwivedi, N. Ho, K. Khamaru, M. J. Wainwright, M. I. Jordan, and B. Yu · 2020
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