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 [1].
A Safe Screening Rule for Sparse Logistic Regression
Authors: Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have evaluated Slores using high-dimensional data sets from different applications. Experiments demonstrate that Slores outperforms the existing state-of-the-art screening rules and the efficiency of solving sparse logistic regression can be improved by one magnitude. |
| Researcher Affiliation | Collaboration | Jie Wang Arizona State University Tempe, AZ 85287 EMAIL Jiayu Zhou Arizona State University Tempe, AZ 85287 EMAIL Jun Liu SAS Institute Inc. Cary, NC 27513 EMAIL Peter Wonka Arizona State University Tempe, AZ 85287 EMAIL Jieping Ye Arizona State University Tempe, AZ 85287 EMAIL |
| Pseudocode | Yes | Algorithm 1 R = Slores(X, b, λ, λ0, θ λ0) |
| Open Source Code | No | The paper does not provide an explicit link or statement about the open-source availability of the code for the Slores method described. It mentions SLEP [14] as a tool used, but not its own implementation. |
| Open Datasets | Yes | We evaluate our screening rules using the newgroup data set [10] and Yahoo web pages data sets [23]. |
| Dataset Splits | No | The paper mentions "undersample 80% of the data" and repeating procedures, but does not explicitly define traditional training/validation/test splits with percentages or sample counts for model development or tuning within a single experimental run. |
| Hardware Specification | Yes | All of the experiments are carried out on a Intel(R) (i7-2600) 3.4Ghz processor. |
| Software Dependencies | No | Slores, strong rules and SAFE are all implemented in Matlab. All of the experiments are carried out on a Intel(R) (i7-2600) 3.4Ghz processor. The paper mentions MATLAB but does not provide a specific version number. It also mentions SLEP [14] but without a version. |
| Experiment Setup | Yes | All of the screening rules are tested along a sequence of 86 parameter values equally spaced on the λ/λmax scale from 0.1 to 0.95. We repeat the procedure 100 times and during each time we undersample 80% of the data. |