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 [1].
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
Authors: Eunho Yang, Aurelie C. Lozano
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are complemented by experiments on simulated and real gene expression data which further demonstrate the value of our approach. |
| Researcher Affiliation | Industry | Eunho Yang IBM T.J. Watson Research Center EMAIL Aurelie C. Lozano IBM T.J. Watson Research Center EMAIL |
| Pseudocode | Yes | Algorithm 1 Trimmed Graphical Lasso in (3) |
| Open Source Code | No | The paper mentions using 'R implementations provided by the methods authors' for comparison methods and refers to supplementary material for proofs, but does not provide concrete access to its own source code. |
| Open Datasets | Yes | We analyze a yeast microarray dataset generated by [28]. |
| Dataset Splits | Yes | We use 5-fold-CV to choose the tuning parameters for each method. |
| Hardware Specification | Yes | Experiments were run on R in a single computing node with a Intel Core i5 2.5GHz CPU and 8G memory. |
| Software Dependencies | No | The paper mentions 'R' and various R packages (t-lasso, t*-lasso, robust LL, glassopath) used for the experiments but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | For the robust-LL method we set β = 0.05 and for trimglasso we use h/n = 80%. We use 5-fold-CV to choose the tuning parameters for each method. |