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

arXiv: https://arxiv.org/abs/2011.12392 · HTML: https://mlsanity.com/paper/2011.12392/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning](https://mlsanity.com/paper/2302.11147/) (2023)
- [Federated Expectation Maximization with heterogeneity mitigation and variance reduction](https://mlsanity.com/paper/2111.02083/) (2021)
- [Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization](https://mlsanity.com/paper/2301.00631/) (2023)
- [The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning](https://mlsanity.com/paper/2105.11732/) (2021)
- [A Class of Two-Timescale Stochastic EM Algorithms for Nonconvex Latent Variable Models](https://mlsanity.com/paper/2203.10186/) (2022)
- [Revisiting Incremental Stochastic Majorization-Minimization Algorithms with Applications to Mixture of Experts](https://mlsanity.com/paper/2601.19811/) (2026)
- [Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Models](https://mlsanity.com/paper/2602.07997/) (2026)

## Abstract (arXiv)

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.