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..
Beyond L1: Faster and Better Sparse Models with skglm
Authors: Quentin Bertrand, Quentin Klopfenstein, Pierre-Antoine Bannier, Gauthier Gidel, Mathurin Massias
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide an extensive experimental comparison and we show state-of-the-art improvements on a wide range of convex and non-convex problems. |
| Researcher Affiliation | Collaboration | Quentin Bertrand Mila & Ude M, Canada EMAIL Quentin Klopfenstein Luxembourg Centre for Systems Biomedicine University of Luxembourg Esch-sur-Alzette, Luxembourg Pierre-Antoine Bannier Independent Researcher Gauthier Gidel Mila & Ude M, Canada CIFAR AI Chair Mathurin Massias Univ. Lyon, Inria, CNRS, ENS de Lyon, UCB Lyon 1, LIP UMR 5668, F-69342 Lyon, France |
| Pseudocode | Yes | Algorithm 1 skglm (proposed) input : X, β Rp, nout N, nin N, ws_size N, ϵ > 0 |
| Open Source Code | Yes | We release skglm, a flexible, scikit-learn compatible package, which easily handles customized datafits and penalties. |
| Open Datasets | Yes | We use datasets from libsvm4 (Fan et al. 2008, see table 2). |
| Dataset Splits | No | The paper states 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 3.', however, Section 3 describes the benchmarking process and dataset usage but does not explicitly detail train/validation/test splits, percentages, or cross-validation methodology for the models themselves. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Software Dependencies | No | Our package relying on numpy and numba (Lam et al., 2015; Harris et al., 2020) is attached in the supplementary material. No version numbers are given for these dependencies. |
| Experiment Setup | Yes | skglm (Algorithm 1, ours), using M = 5 iterates for the Anderson extrapolation. |