Robust Gaussian Graphical Model Estimation with Arbitrary Corruption
Authors: Lingxiao Wang, Quanquan Gu
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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. |