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..
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
Authors: Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that our method is very flexible and performs well compared to Hogwild-type algorithms. |
| Researcher Affiliation | Academia | Andrei-Cristian B arbos IMS Laboratory Univ. Bordeaux CNRS BINP EMAIL François Caron Department of Statistics University of Oxford EMAIL Jean-François Giovannelli IMS Laboratory Univ. Bordeaux CNRS BINP EMAIL Arnaud Doucet Department of Statistics University of Oxford EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes an application to image inpainting-deconvolution using an 'unobserved image... of size 1000x1000', but does not provide access information (link, DOI, specific citation) for a publicly available dataset. |
| Dataset Splits | No | The paper discusses 'burn-in samples' for MCMC, but does not specify dataset splits (e.g., percentages or counts for training, validation, or test sets). |
| Hardware Specification | Yes | Experiments are run on GPU with 2688 CUDA cores. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | The tuning parameter η is set to 1. We run our clone MCMC algorithm for ns = 19000 samples, out of which the first 4000 were discarded as burn-in samples, using as initialization the observed image, with missing entries padded with zero. The observation noise is assumed to be independent of X with Σ 1 b = γb I and γb = 10 2. |