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..
Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data
Authors: Xixian Chen, Michael R. Lyu, Irwin King
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, the extensive experiments on synthetic and real-world datasets validate the superior property of our method and illustrate that it significantly outperforms the state-of-the-art algorithms.4. Empirical Studies In this section, we empirically verify the properties of the proposed method and demonstrate its superiority. We compare its estimation accuracy with that of Gauss-Inverse, Sparse, and Uni Sample-HD. We also report the time comparisons. |
| Researcher Affiliation | Academia | 1Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China. 2Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. |
| Pseudocode | Yes | Algorithm 1 The proposed algorithm. |
| Open Source Code | No | The paper mentions implementing algorithms in C++ for comparison but does not provide a link or explicit statement about releasing the source code for their proposed methodology. |
| Open Datasets | Yes | We use nine publicly available real-world datasets (Chang & Lin, 2011; Blake & Merz, 1998; Amsaleg, 2010) |
| Dataset Splits | No | The paper uses synthetic and real-world datasets but does not explicitly provide details about training, validation, or test splits (e.g., percentages, sample counts, or specific split files). |
| Hardware Specification | Yes | We implement all algorithms in C++ and run them in a single thread mode on a standard workstation with Intel CPU@2.90GHz and 128GB RAM. |
| Software Dependencies | No | The paper states that algorithms are implemented in C++ but does not provide specific version numbers for any ancillary software, libraries, or solvers used. |
| Experiment Setup | Yes | The parameter selection on α is deferred to the appendix, and we empirically set α = 0.9. |