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..
GAP Safe screening rules for sparse multi-task and multi-class models
Authors: Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we derive new safe rules for generalized linear models regularized with ℓ1 and ℓ1{ℓ2 norms. ... The GAP Safe rule can cope with any iterative solver and we illustrate its performance on coordinate descent for multi-task Lasso, binary and multinomial logistic regression, demonstrating significant speed ups on all tested datasets with respect to previous safe rules. |
| Researcher Affiliation | Academia | LTCI, CNRS, T el ecom Paris Tech, Universit e Paris-Saclay Paris, 75013, France |
| Pseudocode | No | In all experiments, the coordinate descent algorithm used follows the pseudo code from [11] with a screening step every 10 iterations. |
| Open Source Code | No | The paper mentions that their implementation is based on Scikit-Learn and Lightning software, but does not explicitly state that their specific GAP Safe rule implementation is open-source or provide a link to it. |
| Open Datasets | No | The paper mentions 'MEG/EEG brain imaging dataset', 'Leukemia dataset', and 'News20 dataset', but does not provide concrete access information (link, DOI, specific repository, or formal citation with author/year for public access) for the exact datasets used. |
| Dataset Splits | No | The paper does not explicitly state specific training/validation/test dataset splits, sample counts, or cross-validation methodologies used to partition the data for reproduction. |
| Hardware Specification | No | The paper does not specify any details about the hardware used for running experiments (e.g., CPU/GPU models, memory, or specific computing environments). |
| Software Dependencies | No | The paper mentions 'Python and Cython', 'Scikit-Learn [17]', and 'Lightning software [4]' as software used, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | In all experiments, the coordinate descent algorithm used follows the pseudo code from [11] with a screening step every 10 iterations. The experimental setup consists in estimating the solutions of the multi-task Lasso problem for 100 values of λ on a logarithmic grid from λmax to λmax{103. |