Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Scalable Sparse Covariance Estimation via Self-Concordance
Authors: Anastasios Kyrillidis, Rabeeh Karimi Mahabadi, Quoc Tran Dinh, Volkan Cevher
AAAI 2014 | Venue PDF | LLM Run Details | Input Tokens: 15,760 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,834 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 )). |