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Fast Lasso Algorithm via Selective Coordinate Descent
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| selectively update only predictors that must or can have nonzero weights using z bounds | lasso coordinate descent · DNA, Protein, Reuters, TDT2, Newsgroups | processing time: up to 70% less time | Helped |
| selective updates (Sling) vs standard | lasso, full-column-rank X · DNA | squared loss: 5.097×10−2 -> 5.097×10−2 | No clear effect |
| selective updates (Sling) vs standard | lasso, p>n · Reuters | squared loss: 5.006×10−6 -> 5.003×10−6 | Helped |
| selective updates (Sling) vs standard | lasso, p>n · TDT2 | squared loss: 1.691×10−6 -> 1.679×10−6 | Helped |
| selective updates (Sling) vs standard | lasso, p>n · Newsgroups | squared loss: 3.478×10−6 -> 3.463×10−6 | Helped |
Closest papers by meaning
- Approximate Steepest Coordinate Descent (ICML 2017 · similarity 0.85)
- Fast Sparse Group Lasso (NeurIPS 2019 · similarity 0.83)
- Celer: a Fast Solver for the Lasso with Dual Extrapolation (ICML 2018 · similarity 0.83)
- Screening rules for Lasso with non-convex Sparse Regularizers (ICML 2019 · similarity 0.82)
- Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm (ICML 2022 · similarity 0.82)
- Implicit differentiation of Lasso-type models for hyperparameter optimization (ICML 2020 · similarity 0.82)
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