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We address the problem of solving mixed random linear equations.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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A mixture likelihood approach for generalized linear models
Michel Wedel and Wayne S DeSarbo · 1995
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On convergence properties of the EM algorithm for Gaussian mixtures
Lei Xu and Michael I Jordan · 1996
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Estimates of use and costs of behavioural health care: a comparison of standard and finite mixture models
Partha Deb and Ann M. Holmes · 2000
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Modeling with mixtures of linear regressions
Kert Viele and Barbara Tong · 2002
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Applications of finite mixtures of regression models
Bettina Grün, Friedrich Leisch, et al · 2007
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
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Market segmentation: Conceptual and methodological foundations
Michel Wedel and Wagner A Kamakura · 2012
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Spectral experts for estimating mixtures of linear regressions
Arun Tejasvi Chaganty and Percy Liang · 2013
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Phaselift: Exact and stable signal recovery from magnitude measurements via convex programming
Emmanuel J Candes, Thomas Strohmer, and Vladislav Voroninski · 2013
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Low-rank matrix completion using alternating minimization
Prateek Jain, Praneeth Netrapalli, and Sujay Sanghavi · 2013
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Phase retrieval using alternating minimization
Praneeth Netrapalli, Prateek Jain, and Sujay Sanghavi · 2013
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A convex formulation for mixed regression with two components: Minimax optimal rates
Yudong Chen, Xinyang Yi, and Constantine Caramanis · 2014
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Alternating minimization for mixed linear regression
Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2014
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High dimensional em algorithm: Statistical optimization and asymptotic normality
Zhaoran Wang, Quanquan Gu, Yang Ning, and Han Liu · 2015
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Provable tensor methods for learning mixtures of generalized linear models
Hanie Sedghi, Majid Janzamin, and Anima Anandkumar · 2016
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An improved analysis of alternating minimization for structured multi-response regression
Sheng Chen and Arindam Banerjee · 2018
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Covariate adjusted precision matrix estimation via nonconvex optimization
Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, and Quanquan Gu · 2018
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Learning mixtures of linear regressions with nearly optimal complexity
Yuanzhi Li and Yingyu Liang · 2018
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Globally consistent algorithms for mixture of experts
Ashok Vardhan Makkuva, Sreeram Kannan, and Pramod Viswanath · 2018
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High-dimensional probability: An introduction with applications in data science
Roman Vershynin · 2018
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Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2016
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Mixed linear regression with multiple components
Kai Zhong, Prateek Jain, and Inderjit S Dhillon · 2016
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Reshaped wirtinger flow for solving quadratic system of equations
Huishuai Zhang and Yingbin Liang · 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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Ten steps of EM suffice for mixtures of two Gaussians
Constantinos Daskalakis, Christos Tzamos, and Manolis Zampetakis · 2017
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High-dimensional variance-reduced stochastic gradient expectation-maximization algorithm
Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, and Quanquan Gu · 2017
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Phase retrieval with random Gaussian sensing vectors by alternating projections
Irène Waldspurger · 2018
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Max-affine regression: Provable, tractable, and near-optimal statistical estimation
Avishek Ghosh, Ashwin Pananjady, Aditya Guntuboyina, and Kannan Ramchandran · 2019
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EM converges for a mixture of many linear regressions
Jeongyeol Kwon and Constantine Caramanis · 2019
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Estimating the coefficients of a mixture of two linear regressions by expectation maximization
Jason M. Klusowski, Dana Yang, and W. D. Brinda · 2019
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Iterative least trimmed squares for mixed linear regression
Yanyao Shen and Sujay Sanghavi · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Martin J Wainwright · 2019
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