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..
Equivalence of Graphical Lasso and Thresholding for Sparse Graphs
Authors: Somayeh Sojoudi
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations on random systems are provided in Section 3. Two case studies on fMRI data and electrical circuits are conducted in Sections 4 and 5, respectively. |
| Researcher Affiliation | Academia | Somayeh Sojoudi, EMAIL Department of Electrical Engineering and Computer Sciences University of California, Berkeley |
| Pseudocode | No | The paper describes mathematical derivations and conditions for equivalence. It does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | These fMRI data sets are borrowed form Vertes et al. (2012). |
| Dataset Splits | No | The paper mentions data properties like 'Each data set includes 134 samples of the low frequency oscillations, taken at 140 cortical brain regions' and 'For r = 99 and 10 different trials, we have calculated the sample covariance matrices', but does not provide specific train/test/validation dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We choose the regularization parameter λ in the graphical lasso algorithm and the level of thresholding in such a way that they both lead to graphs with n - 1 = 139 edges. |