Research map / Learning theory and algorithms / Matrix and tensor methods
Sparse Support Recovery with Non-smooth Loss Functions
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ℓ2 data fidelity instead of ℓ1 or ℓ∞ data fidelity | ℓ1-regularized sparse recovery · Compressed sensing simulation | support stability | Helped |
| Use ℓ1 data fidelity instead of ℓ∞ data fidelity | ℓ1-regularized sparse recovery · Compressed sensing simulation | support stability for small support excess | Helped |
| Use ℓ∞ data fidelity | ℓ1-regularized sparse recovery · Compressed sensing simulation | probability of perfect support stability: 0 | Hurt |
| Move the loss norm away from ℓ2 toward either ℓ1 or ℓ∞ | ℓ1-regularized sparse recovery · Compressed sensing simulation | support stability | Hurt |
Closest papers by meaning
- Estimating Sparse Signals With Smooth Support via Convex Programming and Block Sparsity (CVPR 2016 · similarity 0.89)
- Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm (ICML 2024 · similarity 0.89)
- The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements (NeurIPS 2025 · similarity 0.88)
- Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery (AAAI 2024 · similarity 0.87)
- Robust Sparse Regression with Non-Isotropic Designs (NeurIPS 2024 · similarity 0.87)
- Beyond Worst-Case Dimensionality Reduction for Sparse Vectors (ICLR 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.