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..
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
Authors: Jie Wang, Jieping Ye
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real data sets show that TLFre improves the efficiency of SGL by orders of magnitude. |
| Researcher Affiliation | Academia | Jie Wang, Jieping Ye Computer Science and Engineering Arizona State University, Tempe, AZ 85287 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using a third-party solver (SLEP [9]) but does not provide concrete access to its own source code for the described methodology. |
| Open Datasets | Yes | We perform experiments on the Alzheimer s Disease Neuroimaging Initiative (ADNI) data set (http://adni.loni.usc.edu/). |
| Dataset Splits | No | The paper mentions 'cross validation' as an approach to determine parameter values but does not provide specific details on validation splits or cross-validation setup for its experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'SLEP [9]' as the solver used but does not provide specific version numbers for this or any other software dependency. |
| Experiment Setup | Yes | Given a data set, for illustrative purposes only, we select seven values of α from {tan(ψ) : ψ = 5 , 15 , 30 , 45 , 60 , 75 , 85 }. Then, for each value of α, we run TLFre along a sequence of 100 values of λ equally spaced on the logarithmic scale of λ/λα max from 1 to 0.01. |