Approximate Message Passing with Random Initialization for Phase Retrieval
Yuchen Chen, Yandi Shen, Xingyu Xu
math.ST, stat.ML
Submitted: 2026-08-03
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions
- The high-dimensional asymptotics of first order methods with random data
- Learning single index model with gradient descent: spectral initialization and precise asymptotics
- Optimal Spectral Transitions in High-Dimensional Multi-Index Models
- Robust Learning of Multi-index Models via Iterative Subspace Approximation
- The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models
- Computational-Statistical Gaps in Gaussian Single-Index Models
- The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents
- Optimal Sample Complexity of Subgradient Descent for Amplitude Flow via Non-Lipschitz Matrix Concentration
- Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning
- Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery
- Statistical-Computational Trade-offs in Learning Multi-Index Models via Harmonic Analysis
- A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models
- A non-asymptotic distributional theory of approximate message passing for sparse and robust regression
- Solving Random Systems of Quadratic Equations with Tanh Wirtinger Flow
- Dynamical Decoupling of Generalization and Overfitting in Large Two-Layer Networks
- Phase Transitions for Feature Learning in Neural Networks
- Fundamental computational limits of weak learnability in high-dimensional multi-index models
- The Gaussian min-max theorem in the Presence of Convexity
- Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
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