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..
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Authors: Richard Zhang, Salar Fattahi, Somayeh Sojoudi
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The algorithm is highly efficient in practice: we solve the associated MDMC problems with as many as 200,000 variables to 7-9 digits of accuracy in less than an hour on a standard laptop computer running MATLAB. In Section 4, we present computation results on a suite of test cases. Both synthetic and real-life graphs are considered. |
| Researcher Affiliation | Academia | 1Department of Industrial Engineering and Operations Research, University of California, Berkeley, USA. 2Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA. |
| Pseudocode | Yes | Figure 1. MATLAB code for chordal embedding via its internal approximate minimum degree ordering. Given a sparse matrix (C), compute a chordal embedding (Gt) and the number of added edges (m). p = amd(C); % fill-reducing ordering [h,~,~,~,R] = symbfact(C(p,p)); Gt = R+R'; Gt(p,p) = Gt; m = nnz(R)-nnz(tril(C)); |
| Open Source Code | Yes | MATLAB source code: http://alum.mit.edu/www/ ryz. |
| Open Datasets | Yes | The actual graphs (i.e. the sparsity patterns) for Σ 1 are chosen from Suite Sparse Matrix Collection (Davis & Hu, 2011) a publicly available dataset for large-and-sparse matrices collected from real-world applications. |
| Dataset Splits | No | The paper describes how synthetic and real-life graphs are used, and how samples are generated, but it does not specify explicit training, validation, or test dataset splits (e.g., 80/10/10 percentages or specific sample counts for each split). |
| Hardware Specification | Yes | All experiments are performed on a laptop computer with an Intel Core i7 quad-core 2.50 GHz CPU and 16GB RAM. |
| Software Dependencies | Yes | The reported results are based on a serial implementation in MATLAB-R2017b. |
| Experiment Setup | Yes | Our implementation used γ = 0.01 and ρ = 0.5. Both our Newton decrement threshold and QUIC s convergence threshold are 10 7. In all of our simulations, the threshold λ is set so that the number of nonzero elements in the the estimator is roughly the same as the ground truth. |