Research map / Learning theory and algorithms / Matrix and tensor methods
Support Recovery of Sparse Signals from a Mixture of Linear Measurements
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase repeated queries per distinct query | Support-recovery algorithms · MLC, full-rank support simulation | accuracy: 0.04 at T=5; 0.84 at T=50 (Algorithm 1); 0.0 at T=5; 0.99 at T=50 (Jennrich's algorithm) | Helped |
| Use Jennrich's algorithm rather than Algorithm 1 | Full-rank support recovery · MLC, full-rank support simulation | accuracy: 0.48 vs 0.5 at T=20; 0.45 vs 0.62 at T=25; 0.72 vs 0.8 at T=30; 0.86 vs 0.9 at T=35; 0.87 vs 0.96 at T=40; 0.89 vs 0.99 | Mixed |
Closest papers by meaning
- Recovery of sparse linear classifiers from mixture of responses (NeurIPS 2020 · similarity 0.91)
- Recovery of Sparse Signals from a Mixture of Linear Samples (ICML 2020 · similarity 0.90)
- On learning sparse vectors from mixture of responses (NeurIPS 2021 · similarity 0.90)
- Exact Recovery of Sparse Binary Vectors from Generalized Linear Measurements (ICML 2025 · similarity 0.88)
- The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements (NeurIPS 2025 · similarity 0.87)
- Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm (ICML 2024 · similarity 0.86)
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