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..
Estimation of Graphical Models through Structured Norm Minimization
Authors: Davoud Ataee Tarzanagh, George Michailidis
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the superior performance of the proposed framework on a number of synthetic data sets generated from both random and structured networks. Further, we apply the method to a number of real data sets and discuss the results. |
| Researcher Affiliation | Academia | Davoud Ataee Tarzanagh EMAIL Department of Mathematics UF Informatics Institute University of Florida Gainesville, FL 32611-8105, USA; George Michailidis EMAIL Department of Statistics UF Informatics Institute University of Florida Gainesville, FL 32611-8545, USA |
| Pseudocode | Yes | Algorithm 1 Multi-Block ADMM Algorithm for Solving (19).; Algorithm 2 Non-monotone Barzilai Borwein Method for solving (33) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions using third-party packages (CONTEST, UGM) and refers to supplementary materials for a list of genes, but not for the code itself. |
| Open Datasets | Yes | The data set under study considers gene expression profiles of lung cancer tumors... http://www.broadinstitute.org/cgibin/cancer/publications/view/87.; The next example involves a data set 3 containing 1427 documents... http://qwone.com/~jason/20Newsgroups/.; We use monthly stock returns data...obtained from the University of Chicago s Center for Research in Security Prices database (CRSP). |
| Dataset Splits | Yes | We randomly partition the data into 999 training, 214 validation and 214 test examples, corresponding to a 70/15/15 split (Rao et al., 2016). |
| Hardware Specification | Yes | All the algorithms have been implemented in the MATLAB R2015b environment on a PC with a 1.8 GHz processor and 6GB RAM memory. |
| Software Dependencies | Yes | All the algorithms have been implemented in the MATLAB R2015b environment... The CONTEST 1 package is used to generate the synthetic graphs, and the UGM 2 package to implement Gibbs sampling for estimating the Ising Model. |
| Experiment Setup | Yes | The penalty parameters λe and {λi}n i=1 play an important rule for the convex decomposition to be successful. We learn them through numerical experimentation (see Figures 5 and 6) and set them respectively to ϱ = 4, λe = 1, λ1, λ2 = 0.5λe, ˆλi = 0.25λe, and λi+1 = 2λi for i = 2, . . . , n. ...terminated either when Θk Θk 1 2 F / Θk 1 2 F τ, τ = 1e 5, or the number of iterations and CPU times exceed 1,000 and 10 minutes, respectively. |