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..
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption
Authors: Lingxiao Wang, Quanquan Gu
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our method through extensive experiments on both synthetic data and real-world genomic data. and Section 5 provides numerical results, for our method and a number of other methods, of some simulated datasets and a real example on gene expression data. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA. |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found. |
| Open Source Code | No | The paper mentions that 'The implementation of other baseline algorithms is based on R package huge1' and provides a link to an R package, but does not state that their own code is open-source or provide a link to it. |
| Open Datasets | Yes | In Section 5.2, 'Gene Expression Data', the paper states: 'we use the gene expression data of Arabidopsis thaliana, which was analyzed by Wille et al. (2004) and later on by Finegold & Drton (2011); Hirose & Fujisawa (2015)'. It also preprocesses it 'through R package limma3' with a footnote 'Available on http://bioconductor.org/packages/limma'. |
| Dataset Splits | Yes | We generate a dataset as the training sample, and an independent dataset from the same distribution as the test set. and For Robust CLIME, we set n2 = 10 and adopt 5-fold crossvalidation to choose the tuning parameter λ. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software packages like 'R package huge' and 'R package limma', but it does not specify their version numbers or any other software dependencies with specific version information. |
| Experiment Setup | Yes | For the arbitrary corruption model...we let np coordinates be arbitrarily corrupted, where we consider the corruption rate p = 0.1 for small number of corruptions and p = 0.2 for large number of corruptions. In addition, each corrupted coordinate is generated by normal distributions N(µ, σ) as follows: MA1 : µ = 1, σ = 1, MA2 : µ = 2, σ = 1. For point estimation: We set n2/n = 0.9 for t GLasso, β = 0.01 for RLL, and n2 = np for Robust CLIME. We also choose the tuning parameter λ by grid search based on its performance on the training sample and evaluate those estimators on the test set. |