Safe Screening with Variational Inequalities and Its Application to Lasso
Authors: Jun Liu, Zheng Zhao, Jie Wang, Jieping Ye
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real data sets are reported to demonstrate the effectiveness of the proposed Sasvi for Lasso screening. |
| Researcher Affiliation | Collaboration | Jun Liu, Zheng Zhao {JUN.LIU,ZHENG.ZHAO}@SAS.COM SAS Institute Inc., Cary, NC 27513 Jie Wang, Jieping Ye {JIE.WANG.USTC,JIEPING.YE}@ASU.EDU Arizona State University, Tempe, AZ 85287 |
| Pseudocode | No | The paper contains mathematical derivations and theoretical explanations, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper states: "For the Lasso solver, we make use of the SLEP package (Liu et al., 2009). URL http://www.public.asu.edu/~jye02/Software/SLEP." This link is to a third-party solver used by the authors, not the open-source code for the Sasvi method itself. |
| Open Datasets | Yes | PIE Face Image Data Set The PIE face image data set used in this experiment 4 contains 11554 gray face images... 4http://www.cad.zju.edu.cn/home/dengcai/ Data/Face Data.html. MNIST Handwritten Digit Data Set This data set contains grey images of scanned handwritten digits, including 60, 000 for training and 10, 000 for testing. |
| Dataset Splits | No | The paper describes how synthetic data is generated and how the PIE and MNIST datasets are used to construct X and y. For example, for MNIST: "we first randomly select 5000 images for each digit from the training set (and in total we have 50000 images) and get a data matrix X R784 50000, and then we randomly select an image from the testing set and treat it as the response vector y R784." While it uses parts of standard datasets, it does not specify explicit training/validation/test splits for the models being evaluated (e.g., percentages or sample counts for each split). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper mentions using the "SLEP package (Liu et al., 2009)" as the Lasso solver. However, it does not specify a version number for SLEP or any other software dependencies, such as programming languages or libraries with their versions. |
| Experiment Setup | Yes | For a given generated data set (X and y), we run the solver with or without screening rules to solve the Lasso problems along a sequence of 100 parameter values equally spaced on the λ/λmax scale from 0.05 to 1.0. The reported results are averaged over 100 trials of randomly drawn X and y. |