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..
Using Large Ensembles of Control Variates for Variational Inference
Authors: Tomas Geffner, Justin Domke
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results show that combining a large number of control variates this way significantly improves the convergence of inference over using the typical gradient estimators or a reduced number of control variates. |
| Researcher Affiliation | Academia | Tomas Geffner College of Information and Computer Science University of Massachusetts Amherst, MA 01003 EMAIL Domke College of Information and Computer Science University of Massachusetts Amherst, MA 01003 EMAIL |
| Pseudocode | No | The paper describes algorithmic steps in prose (e.g., the update rule for exponential averages), but does not present a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to code repositories for the described methodology. |
| Open Datasets | Yes | We tried several control variates and the combination algorithm on a Bayesian binary logistic regression model with a standard Gaussian prior, using three well known datasets: ionosphere, australian, and sonar. |
| Dataset Splits | No | The paper mentions using "minibatches of size 10" but does not specify explicit training, validation, or test dataset splits in terms of percentages, sample counts, or predefined split references. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., GPU/CPU models, cloud instances, or detailed computer specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | We use simple SGD with momentum (β = 0.9) as our optimization algorithm, minibatches of size 10, a decay factor of γ = 0.02 for the exponentially decayed empirical averages, and v0 = 10 3, value based on results obtained for the sensitivity analysis carried out (see Sec. 5.1). |