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arXiv Machine Learning By Ayub Kharel, Ilja Kuzborski, Patrick Rebeschini, Yasin Abbasi-Yadkori

Generalization in Nonlinear Least Squares via Learned Feature Geometry

Read the original on arXiv Machine Learning →

arXiv:2606. 08799v1 Announce Type: cross Abstract: We study the generalization of ridge-regularized nonlinear least-squares models via on-average algorithmic stability, deriving error bounds for local minimizers in terms of a data-dependent effective dimension that reflects the geometry of the gradient model at the trained parameters, through the empirical Jacobian Gram matrix and a residual--curvature term.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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