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New research offers sharp risk bounds for early-stopping in Gaussian linear regression

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new theoretical bounds for early-stopping in Gaussian linear regression, a technique used to minimize in-sample mean squared error. The study shows that these bounds can match the sharpest known risk bounds for the least squares estimator under specific conditions related to the potential function and Minkowski functional. This work provides a systematic comparison with existing methods and establishes new tight risk bounds, particularly in the context of $\ell_1$-constrained regression. AI

IMPACT Provides theoretical underpinnings for optimizing model training in linear regression settings.

RANK_REASON Academic paper detailing theoretical advancements in statistical machine learning.

Read on arXiv stat.ML →

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

New research offers sharp risk bounds for early-stopping in Gaussian linear regression

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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Tobias Wegel, Gil Kur, Patrick Rebeschini ·

    Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression

    arXiv:2503.03426v2 Announce Type: replace-cross Abstract: We study early-stopped mirror descent (ESMD) for high-dimensional Gaussian linear regression over arbitrary convex bodies and design matrices, where the task is to minimize the in-sample mean squared error. Our main result…

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