Research map / Optimization / Optimizers
Stochastic Expectation Maximization with Variance Reduction
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add SVRG-style variance reduction to stochastic EM (sEM-vr vs sEM) | pLSA stochastic EM · pLSA on NIPS, NYTimes, Wiki, PubMed | training objective vs time | Helped |
| add variance reduction to stochastic EM (sEM-vr vs sEM) | LDA (K=10) stochastic EM · NIPS | test perplexity vs time | Mixed |
| add variance reduction to stochastic EM (sEM-vr vs sEM) | Toy Gaussian mixture · Toy GMM (N=10,000) | log10 E||mu - mu*||^2 | Helped |
| add SVRG-style variance reduction to gradient-based stochastic methods (SMD-vr, RSGD-vr) | SMD / RSGD for pLSA · NIPS pLSA | training objective vs time | No clear effect |
| use EM-based sEM-vr instead of GOEM with K=100 topics | LDA · LDA K=100 | test perplexity | Hurt |
Closest papers by meaning
- High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm (ICML 2017 · similarity 0.91)
- On the Global Convergence of (Fast) Incremental Expectation Maximization Methods (NeurIPS 2019 · similarity 0.88)
- A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm (NeurIPS 2020 · similarity 0.88)
- Toward Global Convergence of Gradient EM for Over-Paramterized Gaussian Mixture Models (NeurIPS 2024 · similarity 0.87)
- Big Learning Expectation Maximization (AAAI 2024 · similarity 0.86)
- Learning Mixtures of Experts with EM: A Mirror Descent Perspective (ICML 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.