mlsanityData to 14 Oct 2026

Approximate Message Passing with Random Initialization for Phase Retrieval

arXiv 2608.01654 ↗First posted 3 Aug 2026math.ST stat.ML stat.THcited by 0

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Abstract (arXiv)

We analyze approximate message passing (AMP) with an independent Gaussian initialization for noiseless phase retrieval in the proportional asymptotic regime. A random initialization has overlap of order d-1/2 with the signal, and AMP requires a growing number of iterations to attain non-vanishing overlap. Thus, its precise behavior cannot be characterized by classical fixed-time state evolution. We prove a Gaussian decomposition of the AMP trajectory and control its error over the horizons required for recovery. The resulting analysis shows that random initialization attains the weak-recovery threshold δrm weak=1/2. For δ∈(δrm weak,δrm str), where δrm str≈1.13, the signal strength follows state evolution and approaches its stable finite fixed point uniformly for (n1/3/operatornamepolylog(n)) iterations. For δ>δrm str, AMP reaches any prescribed fixed recovery accuracy within Oδ,varepsilon(log n) iterations. The majority of our analysis applies more generally to generalized AMP for single-index models.

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References · 18

  1. 2024The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponentscited by 52
  2. 2024Computational-Statistical Gaps in Gaussian Single-Index Modelscited by 44
  3. 2024Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functionscited by 37
  4. 2025Dynamical Decoupling of Generalization and Overfitting in Large Two-Layer Networkscited by 25
  5. 2022A Non-Asymptotic Framework for Approximate Message Passing in Spiked Modelscited by 20
  6. 2024Fundamental computational limits of weak learnability in high-dimensional multi-index modelscited by 19
  7. 2026Phase Transitions for Feature Learning in Neural Networkscited by 11
  8. 2025Robust Learning of Multi-index Models via Iterative Subspace Approximationcited by 9
  9. 2025The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Modelscited by 9
  10. 2025Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recoverycited by 8
  11. 2025Optimal Spectral Transitions in High-Dimensional Multi-Index Modelscited by 7
  12. 2025Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limitcited by 6
6 more
  1. 2024A non-asymptotic distributional theory of approximate message passing for sparse and robust regressioncited by 4
  2. 2026Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learningcited by 3
  3. 2020Optimal Sample Complexity of Subgradient Descent for Amplitude Flow via Non-Lipschitz Matrix Concentrationcited by 2
  4. 2025Learning single index model with gradient descent: spectral initialization and precise asymptoticscited by 2
  5. 2026Statistical-Computational Trade-offs in Learning Multi-Index Models via Harmonic Analysiscited by 2
  6. 2019Solving Random Systems of Quadratic Equations with Tanh Wirtinger Flowcited by 1

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