Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm
Authors: Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | demonstrate the advantages of our algorithm by both theoretical analysis and numerical experiments. and In this section, we present experiment results to validate our theory. |
| Researcher Affiliation | Collaboration | 1Facebook, Inc., Menlo Park, CA 94025 2Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA 3Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801. |
| Pseudocode | Yes | Algorithm 1 Variance Reduced Stochastic Gradient EM Algorithm (VRSGEM) |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic data for experiments based on specified parameters (e.g., 'The covariance matrix Σ of V is chosen to be a diagonal matrix with all elements being 1. We randomly set two elements to λmax(Σ) = 10, and another two elements to λmin(Σ) = 0.1.'), but does not provide access information (link, DOI, or citation) to a publicly available dataset. |
| Dataset Splits | No | The paper describes overall experiment settings including sample sizes (N), but does not explicitly provide specific train/validation/test dataset splits (percentages or counts) or refer to standard predefined splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software dependencies (e.g., programming languages, libraries, or frameworks) used in the experiments. |
| Experiment Setup | Yes | All the comparisons are under two different parameter settings: s = 5, d = 256, b = 100, N = 5000 and s = 10, d = 512, b = 200, N = 10000. For VRSGEM, we choose m = 30, n = 50 and T = 50 across all settings and models. ... The learning rate η is tuned by grid search and s is chosen by cross validation. We use random initialization. |