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On the robust learning mixtures of linear regressions

arXiv 2305.15317 ↗First posted 23 May 2023stat.ML cs.LGcited by 0

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Abstract (arXiv)

In this note, we consider the problem of robust learning mixtures of linear regressions. We connect mixtures of linear regressions and mixtures of Gaussians with a simple thresholding, so that a quasi-polynomial time algorithm can be obtained under some mild separation condition. This algorithm has significantly better robustness than the previous result.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 23 May 2023.

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References · 9

  1. 2014Statistical guarantees for the EM algorithm: From population to sample-based analysiscited by 133
  2. 2017Mixture Models, Robustness, and Sum of Squares Proofscited by 81
  3. 2017List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussianscited by 70
  4. 2018Learning Mixtures of Linear Regressions with Nearly Optimal Complexitycited by 37
  5. 2020Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Modelscited by 28
  6. 2019EM Converges for a Mixture of Many Linear Regressionscited by 27
  7. 2019Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Momentscited by 26
  8. 2013Spectral Experts for Estimating Mixtures of Linear Regressionscited by 22
  9. 2017Estimating the Coefficients of a Mixture of Two Linear Regressions by Expectation Maximizationcited by 19

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