Fetching the paper…
Reading the bibliography…
We consider the problem of learning a mixture of linear regressions (MLRs).
Contribution à l’étude de la représentation d’une fonction arbitraire par des intégrales définies
Michel Plancherel and Mittag Leffler · 1910
Earlier work this paper cites.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
Earlier work this paper cites.
Mixtures of linear regressions
Richard D De Veaux · 1989
Earlier work this paper cites.
Hierarchical mixtures of experts and the em algorithm
Michael I Jordan and Robert A Jacobs · 1994
Earlier work this paper cites.
Optimum bounds for the distributions of martingales in banach spaces
Iosif Pinelis et al · 1994
Earlier work this paper cites.
Trajectory clustering with mixtures of regression models
Scott Gaffney and Padhraic Smyth · 1999
Earlier work this paper cites.
Subspace clustering for high dimensional data: a review
Lance Parsons, Ehtesham Haque, and Huan Liu · 2004
Earlier work this paper cites.
Generalized principal component analysis (gpca)
Rene Vidal, Yi Ma, and Shankar Sastry · 2005
Earlier work this paper cites.
On signal reconstruction without phase
Radu Balan, Pete Casazza, and Dan Edidin · 2006
Earlier work this paper cites.
Three-view multibody structure from motion
Rene Vidal and Richard Hartley · 2007
Earlier work this paper cites.
A randomized algorithm for principal component analysis
Vladimir Rokhlin, Arthur Szlam, and Mark Tygert · 2009
Earlier work this paper cites.
Fitting mixtures of linear regressions
Susana Faria and Gilda Soromenho · 2010
Earlier work this paper cites.
Settling the polynomial learnability of mixtures of gaussians
Ankur Moitra and Gregory Valiant · 2010
Earlier work this paper cites.
Identification of switched linear systems via sparse optimization
Laurent Bako · 2011
Earlier work this paper cites.
Subspace clustering
René Vidal · 2011
Earlier work this paper cites.
Nearly optimal sparse Fourier transform
Haitham Hassanieh, Piotr Indyk, Dina Katabi, and Eric Price · 2012
Earlier work this paper cites.
Robust recovery of subspace structures by low-rank representation
Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, and Yi Ma · 2012
Earlier work this paper cites.
Robust and efficient subspace segmentation via least squares regression
Can-Yi Lu, Hai Min, Zhong-Qiu Zhao, Lin Zhu, De-Shuang Huang, and Shuicheng Yan · 2012
Earlier work this paper cites.
Spectral experts for estimating mixtures of linear regressions
Arun Tejasvi Chaganty and Percy Liang · 2013
Cited alongside, same era.
Phaselift: Exact and stable signal recovery from magnitude measurements via convex programming
Emmanuel J Candes, Thomas Strohmer, and Vladislav Voroninski · 2013
Cited alongside, same era.
A convex formulation for mixed regression with two components: Minimax optimal rates
Yudong Chen, Xinyang Yi, and Constantine Caramanis · 2013
Cited alongside, same era.
Sparse subspace clustering: Algorithm, theory, and applications
Ehsan Elhamifar and Rene Vidal · 2013
Cited alongside, same era.
Phase retrieval using alternating minimization
Praneeth Netrapalli, Prateek Jain, and Sujay Sanghavi · 2013
Cited alongside, same era.
Efficient density estimation via piecewise polynomial approximation
Provable tensor methods for learning mixtures of generalized linear models
Hanie Sedghi, Majid Janzamin, and Anima Anandkumar · 2016
Later among the works it cites.
Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2016
Later among the works it cites.
Mixed linear regression with multiple components
Kai Zhong, Prateek Jain, and Inderjit S Dhillon · 2016
Later among the works it cites.
Sample-optimal density estimation in nearly-linear time
Jayadev Acharya, Ilias Diakonikolas, Jerry Li, and Ludwig Schmidt · 2017
Later among the works it cites.
Statistical guarantees for the EM algorithm: From population to sample-based analysis
Sivaraman Balakrishnan, Martin J Wainwright, and Bin Yu · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Siu-On Chan, Ilias Diakonikolas, Rocco A Servedio, and Xiaorui Sun · 2014
Cited alongside, same era.
Sample-optimal Fourier sampling in any constant dimension
Piotr Indyk and Michael Kapralov · 2014
Cited alongside, same era.
Alternating minimization for mixed linear regression
Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2014
Cited alongside, same era.
Tight bounds for learning a mixture of two Gaussians
Moritz Hardt and Eric Price · 2015
Cited alongside, same era.
The threshold for super-resolution via extremal functions
Ankur Moitra · 2015
Cited alongside, same era.
A robust sparse Fourier transform in the continuous setting
Eric Price and Zhao Song · 2015
Cited alongside, same era.
Dual principal component pursuit
Manolis C Tsakiris and René Vidal · 2015
Cited alongside, same era.
Learning from untrusted data
Moses Charikar, Jacob Steinhardt, and Gregory Valiant · 2017
Later among the works it cites.
Sample efficient estimation and recovery in sparse FFT via isolation on average
Michael Kapralov · 2017
Later among the works it cites.
Estimating the coefficients of a mixture of two linear regressions by expectation maximization
Jason M Klusowski, Dana Yang, and WD Brinda · 2017
Later among the works it cites.
Hyperplane clustering via dual principal component pursuit
Manolis C Tsakiris and René Vidal · 2017
Later among the works it cites.
Gravitational allocation on the sphere
Nina Holden, Yuval Peres, and Alex Zhai · 2018
Later among the works it cites.
Global convergence of EM algorithm for mixtures of two component linear regression
Jeongyeol Kwon, Wei Qian, Constantine Caramanis, Yudong Chen, and Damek Davis · 2018
Later among the works it cites.
Learning mixtures of linear regressions with nearly optimal complexity
Yuanzhi Li and Yingyu Liang · 2018
Later among the works it cites.
High-dimensional probability: An introduction with applications in data science
Roman Vershynin · 2018
Later among the works it cites.
EM converges for a mixture of many linear regressions
Jeongyeol Kwon and Constantine Caramanis · 2019
Closest in time.
List-decodable linear regression
Sushrut Karmalkar, Pravesh Kothari, and Adam Klivans · 2019
Closest in time.
(Nearly) sample-optimal sparse Fourier transform in any dimension; RIPless and Filterless
Vasileios Nakos, Zhao Song, and Zhengyu Wang · 2019
Closest in time.
List decodable learning via sum of squares
Prasad Raghavendra and Morris Yau · 2019
Closest in time.