<ul data-eligibleForWebStory="false"><li>The study focuses on parameter estimation in high-dimensional generalized linear models.</li><li>They propose spectral methods using the principal eigenvector of a data-dependent matrix for effective solutions.</li><li>The research addresses the challenges of structured real-world data matrices with non-trivial correlations.</li><li>Their approach provides a precise performance characterization and optimal preprocessing for parameter estimation.</li></ul>

Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing

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