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Statistics > Machine Learning

arXiv:2011.12392 (stat)
[Submitted on 24 Nov 2020]

Title:Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization

Authors:Gersende Fort (IMT), Eric Moulines (X-DEP-MATHAPP), Hoi-To Wai
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Abstract:The Expectation Maximization (EM) algorithm is a key reference for inference in latent variable models; unfortunately, its computational cost is prohibitive in the large scale learning setting. In this paper, we propose an extension of the Stochastic Path-Integrated Differential EstimatoR EM (SPIDER-EM) and derive complexity bounds for this novel algorithm, designed to solve smooth nonconvex finite-sum optimization problems. We show that it reaches the same state of the art complexity bounds as SPIDER-EM; and provide conditions for a linear rate of convergence. Numerical results support our findings.
Comments: Submitted to an International conference, with reviewing process
Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
Cite as: arXiv:2011.12392 [stat.ML]
  (or arXiv:2011.12392v1 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.2011.12392

Submission history

From: Gersende Fort [view email] [via CCSD proxy]
[v1] Tue, 24 Nov 2020 21:20:53 UTC (100 KB)
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