Research map / Learning theory and algorithms / Matrix and tensor methods
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add l1 regularization with tuned lambda to PhaseMax for sparse signals | PhaseMax · k-sparse phase retrieval | required measurements: O(k2 log n) -> O(k log(n/k)) | Helped |
| vary regularization parameter lambda toward its maximum acceptable value | regularized PhaseMax · sparse recovery, various rho_init | phase transition (sample complexity) | Mixed |
| increase initial estimate correlation rho_init | PhaseMax · unstructured phase retrieval, n=128 | recovery (phase transition) | Helped |
Closest papers by meaning
- Universality in Learning from Linear Measurements (NeurIPS 2019 · similarity 0.90)
- Convolutional Phase Retrieval (NeurIPS 2017 · similarity 0.88)
- Blind Deconvolutional Phase Retrieval via Convex Programming (NeurIPS 2018 · similarity 0.88)
- A Fast and Provable Algorithm for Sparse Phase Retrieval (ICLR 2024 · similarity 0.86)
- The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements (NeurIPS 2025 · similarity 0.84)
- Exponential Spectral Pursuit: An Effective Initialization Method for Sparse Phase Retrieval (ICML 2024 · similarity 0.84)
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