Gradient descent is one of the most widely used iterative algorithms in modern statistical learning. However, its precise algorithmic dynamics in high-dimensional settings remain only partially understood, which has therefore limited its broader potential for statistical inference applications. This paper provides a precise, non-asymptotic distributional characterization of gradient descent iterates in a broad class of empirical risk minimization problems, in the so-called mean-field regime where the sample size is proportional to the signal dimension. Our non-asymptotic state evolution theory holds for both general non-convex loss functions and non-Gaussian data, and reveals the central role of two Onsager correction matrices that precisely characterize the non-trivial dependence among all gradient descent iterates in the mean-field regime. Although the Onsager correction matrices are typically analytically intractable, our state evolution theory facilitates a generic gradient descent inference algorithm that consistently estimates these matrices across a broad class of models. Leveraging this algorithm, we show that the state evolution can be inverted to construct (i) data-driven estimators for the generalization error of gradient descent iterates and (ii) debiased gradient descent iterates for inference of the unknown signal. Detailed applications to two canonical models--linear regression and (generalized) logistic regression--are worked out to illustrate model-specific features of our general theory and inference methods.

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