New method analyzes generalization in nonlinear least-squares models

By PulseAugur Editorial · [2 sources] · 2026-06-07 19:33

Researchers have developed a new method to understand how nonlinear least-squares models generalize. Their approach uses on-average algorithmic stability to derive error bounds for local minimizers. These bounds are linked to the geometry of the gradient model at the trained parameters, offering insights that depend on learned geometry rather than just parameter count. AI

IMPACT Provides theoretical grounding for understanding model generalization, potentially informing future model development.

RANK_REASON Academic paper published on arXiv.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

arXiv stat.ML TIER_1 English(EN) · Ayub Kharel, Ilja Kuzborski, Patrick Rebeschini, Yasin Abbasi-Yadkori · 2026-06-09 04:00

Generalization in Nonlinear Least Squares via Learned Feature Geometry

arXiv:2606.08799v1 Announce Type: new 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 geom…
arXiv stat.ML TIER_1 English(EN) · Yasin Abbasi-Yadkori · 2026-06-07 19:33

Generalization in Nonlinear Least Squares via Learned Feature Geometry

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 parame…

COVERAGE [2]

Generalization in Nonlinear Least Squares via Learned Feature Geometry

Generalization in Nonlinear Least Squares via Learned Feature Geometry

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