Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Authors: Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough experiments on both synthetic and real data support our theory. (from Abstract) and Section 5 Experiments (section title). |
| Researcher Affiliation | Academia | Jinghui Chen 1 Pan Xu 2 Lingxiao Wang 2 Jian Ma 3 Quanquan Gu 2 1Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA 2Department of Computer Science, University of California, Los Angeles, CA 90095, USA 3School of Computer Science, Carnegie Mellon University University, Pittsburgh, PA 15213, USA. |
| Pseudocode | Yes | Algorithm 1 Alternating Gradient Descent with Hard Thresholding and Algorithm 2 Initialization |
| Open Source Code | No | No statement regarding the public availability of source code or a link to a code repository was found. |
| Open Datasets | Yes | We demonstrate the effectiveness of our proposed method by applying it on an e QTL dataset (yeast) from Brem & Kruglyak (2005) (from Section 5.2) and The Genotype-Tissue Expression (GTEx) project generated RNA-seq expression data for a large number of human tissues (as of February 2018, there are 11688 samples in more than 53 tissues) (Lonsdale et al., 2013). (from Section 5.3) |
| Dataset Splits | Yes | We utilize five-fold cross validation to select the optimal parameters. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. Only general descriptions of methods were provided. |
| Experiment Setup | Yes | Algorithm 1: Input: Number of iterations T, sparsity s1, s2, step size η1, η2. and Algorithm 2: Input: Regularization parameters λΓ, λ and λu and We utilize five-fold cross validation to select the optimal parameters. |