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..
SVD-Based Screening for the Graphical Lasso
Authors: Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our approach is faster than existing approaches. |
| Researcher Affiliation | Collaboration | NTT Communication Science Laboratories, NTT Software Innovation Center, Osaka University |
| Pseudocode | Yes | Algorithm 1 gives a full description of our approach. |
| Open Source Code | No | The paper does not provide an explicit statement or a link for the open-source code of the proposed method 'Sting'. |
| Open Datasets | Yes | We perform experiments on the datasets of Madelon, ISOLET, Gisette, and Arcene. [...] Details of the datasets are shown in the UC Irvine Machine Learning Repository1. 1https://archive.ics.uci.edu/ml/index.html |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. |
| Hardware Specification | Yes | We conducted all experiments on a Linux 2.70 GHz Intel Xeon server. |
| Software Dependencies | No | The paper states that 'sting... are implemented in C/C++' but does not provide specific version numbers for any libraries or dependencies used by 'Sting'. |
| Experiment Setup | Yes | We set the convergence threshold to 10 4. |