Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection
Authors: Sang Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we address this crucial gap by proposing two proximal gradient methods (CONCORDISTA and CONCORD-FISTA) for performing ℓ1-regularized inverse covariance matrix estimation in the pseudo-likelihood framework. We present timing comparisons with coordinate-wise minimization and demonstrate that our approach yields tremendous payoffs for ℓ1-penalized partial correlation graph estimation outside the Gaussian setting, thus yielding the fastest and most scalable approach for such problems. |
| Researcher Affiliation | Academia | Sang-Yun Oh Computational Research Division Lawrence Berkeley National Lab syoh@lbl.gov Onkar Dalal Stanford University onkar@alumni.stanford.edu Kshitij Khare Department of Statistics University of Florida kdkhare@stat.ufl.edu Bala Rajaratnam Department of Statistics Stanford University brajarat@stanford.edu |
| Pseudocode | Yes | Algorithm 1 CONCORD-ISTA Algorithm 2 CONCORD-FISTA |
| Open Source Code | No | The paper mentions 'gconcord package for R available at http://cran.r-project.org/web/packages/gconcord/' which refers to a baseline implementation. It also states 'Algorithms 1 and 2 were also written in C++ then interfaced to R for testing,' but there is no explicit statement or link indicating that the authors' own source code for CONCORD-ISTA/FISTA is made publicly available. |
| Open Datasets | Yes | In this section, the performance of proposed methods are assessed on a breast cancer dataset [12]. This dataset contains expression levels of 24481 genes on 266 patients with breast cancer. |
| Dataset Splits | No | The paper does not provide specific training, validation, or test dataset splits (e.g., percentages or sample counts). It mentions using synthetic and real datasets, and initial guess/convergence criteria, but not how data was partitioned for training/validation/testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | Yes | For this work, we opted to using Eigen library [11] for its sparse linear algebra routines written in C++. Algorithms 1 and 2 were also written in C++ then interfaced to R for testing. |
| Experiment Setup | Yes | The initial guess, Ω(0), and the convergence criteria was matched to those of coordinate-wise CONCORD implementation. The line search for iteration k starts with an initial step size τ(k,0) and reduces the step with a constant factor c until the new iterate satisfies the sufficient descent condition. Figure 1: Convergence of CONCORD-ISTA and CONCORD-FISTA for threshold subg < 10 5. A choice of p-value < 0.03 yields a reduced dataset with p = 4433 genes. |