Scalable Sparse Covariance Estimation via Self-Concordance
Authors: Anastasios Kyrillidis, Rabeeh Karimi Mahabadi, Quoc Tran Dinh, Volkan Cevher
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on sparse covariance estimation show the merits of our algorithm, both in terms of recovery efficiency and complexity. |
| Researcher Affiliation | Academia | Anastasios Kyrillidis, Rabeeh Karimi Mahabadi, Quoc Tran Dinh and Volkan Cevher Ecole Polytechnique F ed erale de Lausanne {anastasios.kyrillidis,rabeeh.karimimahabadi,quoc.trandinh,volkan.cevher}@epfl.ch |
| Pseudocode | Yes | Algorithm 1 Inexact SCOPT for sparse cov. estimation |
| Open Source Code | No | All approaches are carefully implemented in MATLAB code with no C-coded parts. (No statement of code availability) |
| Open Datasets | Yes | This dataset contains 2833 stocks over a trading period of 1038 days, crawled from the Yahoo Finance website1. 1http://finance.yahoo.com |
| Dataset Splits | No | The paper describes generating synthetic data and using real-world stock data, but does not provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or predefined split citations) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | All approaches are carefully implemented in MATLAB code with no C-coded parts. (No specific version numbers for MATLAB or any libraries are provided) |
| Experiment Setup | Yes | In all cases, we set Imax = 500, γ = 10 10 and = 10 8. ... Without loss of generality, we fix λ = 0.5, = 0.1 for the case n = 100 and, λ = 1.5, = 0.1 for the case n = 2000. ... All algorithms under comparison are initialized with x0 = vec(diag(b )). |