Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
GAP Safe Screening Rules for Sparse-Group Lasso
Authors: Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,195 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,849 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present our experiments and illustrate the numerical benefit of screening rules for the Sparse-Group Lasso. |
| Researcher Affiliation | Academia | Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon LTCI, CNRS, Télécom Paris Tech Université Paris-Saclay 75013 Paris, France EMAIL |
| Pseudocode | Yes | Algorithm 1 Computation of Λpx, α, Rq. |
| Open Source Code | Yes | The source code can be found in https://github.com/Eugene Ndiaye/GAPSAFE_SGL. |
| Open Datasets | Yes | Real dataset: NCEP/NCAR Reanalysis 1 [14] |
| Dataset Splits | Yes | We choose τ in the set t0, 0.1, . . . , 0.9, 1u by splitting in 50% the observations and run a training-test validation procedure. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) were provided for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) were provided. Mentions include 'R glmnet package' and the 'ISTA-BC algorithm' without versions. |
| Experiment Setup | Yes | By default, we choose δ 3 and T 100, following the standard practice when running crossvalidation using sparse models (see R glmnet package [11]). The weights are always chosen as wg ?ng (as in [17]). The expensive computation of the dual gap is not performed at each pass over the data, but only every f ce pass (in practice f ce 10 in all our experiments). |