Research map / Learning theory and algorithms / Matrix and tensor methods
Screening rules for Lasso with non-convex Sparse Regularizers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add screening within weighted Lasso subproblems | MM-screen · Toy regression | runtime: about 5 | Helped |
| Add screening propagation between MM iterations (ablation) | MM-genuine · Toy regression | runtime: around 1.8 | Helped |
| Add screening propagation between MM iterations (ablation) | MM-genuine · Toy regression, low-noise regime | runtime: about 6.5 | Helped |
| Add screening propagation between MM iterations (ablation) | MM-genuine · Newsgroups | runtime: 1.3 and 1.8 | Helped |
| Use MM-screen instead of ncxCD | Non-convex regression solver · Leukemia | runtime: 3 to 5 | Helped |
| Use MM-screen instead of GIST | Non-convex regression solver · Leukemia, high tolerance | runtime: about 4 | Helped |
Closest papers by meaning
- Celer: a Fast Solver for the Lasso with Dual Extrapolation (ICML 2018 · similarity 0.89)
- GAP Safe Screening Rules for Sparse-Group Lasso (NeurIPS 2016 · similarity 0.89)
- The Hessian Screening Rule (NeurIPS 2022 · similarity 0.89)
- The Strong Screening Rule for SLOPE (NeurIPS 2020 · similarity 0.87)
- An Alternating Proximal Splitting Method with Global Convergence for Nonconvex Structured Sparsity Optimization (AAAI 2016 · similarity 0.86)
- Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks (NeurIPS 2024 · similarity 0.84)
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