Research map / Learning theory and algorithms / Matrix and tensor methods
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Block-sparse structure exploited (Block CoPRAM vs standard sparse model) | Block CoPRAM · Phase transitions, n=3000, s=20 and s=30 | sample complexity for recovery | Helped |
| Increasing block length b with fixed sparsity s=20 (block sparsity k=s/b) (ablation) | Block CoPRAM · n=3000, s=20, varying b | phase transition location (recovery probability vs samples) | Helped |
| Increasing signal sparsity from s=20 to s=30 | CoPRAM and baselines · Phase transitions, n=3000 | required number of samples | Hurt |
| Support evolving across iterations (CoPRAM vs prior alt-min with fixed initial support) | CoPRAM · theoretical analysis | ability to correct initialization errors | Helped |
Closest papers by meaning
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- Exponential Spectral Pursuit: An Effective Initialization Method for Sparse Phase Retrieval (ICML 2024 · similarity 0.88)
- Fast Compressive Phase Retrieval under Bounded Noise (AAAI 2017 · similarity 0.87)
- The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements (NeurIPS 2025 · similarity 0.82)
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