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New framework explores universality in non-separable AMP algorithms

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new framework for understanding the universality of non-separable Approximate Message Passing (AMP) algorithms. This work identifies a Bounded Composition Property (BCP) for tensors that enables AMP with polynomial non-linearities to exhibit state evolution applicable to matrices beyond i.i.d. Gaussian entries. The study also formalizes a condition for Lipschitz AMP algorithms to achieve similar universal guarantees, demonstrating that many common non-separable non-linearities meet this criterion. AI

IMPACT Provides theoretical underpinnings for understanding iterative learning algorithms, potentially impacting future AI model development.

RANK_REASON Academic paper on theoretical algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework explores universality in non-separable AMP algorithms

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Academic paper on theoretical algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Max Lovig, Tianhao Wang, Zhou Fan ·

    On Universality of Non-Separable Approximate Message Passing Algorithms

    arXiv:2506.23010v2 Announce Type: replace-cross Abstract: Mean-field characterizations of first-order iterative algorithms -- including Approximate Message Passing (AMP), stochastic and proximal gradient descent, and Langevin diffusions -- have enabled a precise understanding of …

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