Mind the duality gap: safer rules for the Lasso
Authors: Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we applied our GAP SAFE rules with a coordinate descent solver for the Lasso problem. Using standard data-sets, we report the time improvement compared to prior safe rules. We implemented the screening rules of Table 1 based on the coordinate descent in Scikit-learn (Pedregosa et al., 2011). Figures 4, 5 and 6 illustrate results on three datasets. |
| Researcher Affiliation | Academia | Olivier Fercoq OLIVIER.FERCOQ@TELECOM-PARISTECH.FR Alexandre Gramfort ALEXANDRE.GRAMFORT@TELECOM-PARISTECH.FR Joseph Salmon JOSEPH.SALMON@TELECOM-PARISTECH.FR Institut Mines-T el ecom, T el ecom Paris Tech, CNRS LTCI 46 rue Barrault, 75013, Paris, France |
| Pseudocode | Yes | Algorithm 1 Coordinate descent with GAP SAFE rules |
| Open Source Code | No | The paper states, 'We implemented the screening rules of Table 1 based on the coordinate descent in Scikit-learn (Pedregosa et al., 2011).', indicating use of an existing library, but does not explicitly state that the authors' own code for the described methodology is being released or provide a link to it. |
| Open Datasets | Yes | Using standard data-sets, we report the time improvement compared to prior safe rules. Figure 3 presents the proportion of variables screened by several safe rules on the standard Leukemia dataset. Figures 4, 5 and 6 illustrate results on three datasets. Figure 4 presents results on the dense, small scale, Leukemia dataset. Figure 5 presents results on a medium scale sparse dataset obtained with bag of words features extracted from the 20newsgroup dataset (comp.graphics vs. talk.religion.misc with TF-IDF removing English stop words and words occurring only once or more than 95% of the time). Text feature extraction was done using Scikit-Learn. Figure 6 focuses on the large scale sparse RCV1 (Reuters Corpus Volume 1) dataset, cf. (Schmidt et al., 2013). |
| Dataset Splits | No | The paper mentions 'cross-validation' and 'Lasso paths' but does not specify exact percentages, sample counts, or a detailed methodology for dataset splits (e.g., train/validation/test) that would allow for reproduction of the data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper states: 'We implemented the screening rules of Table 1 based on the coordinate descent in Scikit-learn (Pedregosa et al., 2011).' While 'Scikit-learn' is mentioned, no specific version number is provided for it or any other software component used in the experiments. |
| Experiment Setup | Yes | In practice, we perform the dynamic screening tests every f 10 passes through the entire (active) variables. Iterations are stopped when the duality gap is smaller than the target accuracy. For each Lasso path, solutions are obtained for T 100 values of λ s, as detailed in Section 3.3. Remark that the grid used is the default one in both Scikit-Learn and the glmnet R package. |