SVD-Based Screening for the Graphical Lasso
Authors: Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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. |