New research explores benign overfitting in linear classifiers with bias terms

Researchers Yuta Kondo and Hashimoto et al. have extended the analysis of benign overfitting in linear regression models. Their work now includes classifiers with a bias term, a feature previously excluded in similar studies. The addition of an intercept term was found to perturb the Gram matrix of noise, introducing new constraints that affect generalization. The impact of these constraints varies depending on the covariance of the noise, with label noise posing the strongest effect. AI

IMPACT This research refines the theoretical understanding of how linear models generalize, which could inform the development of more robust machine learning algorithms.

RANK_REASON Academic paper on a machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• 2025
• arXiv
• Benign overfitting in linear regression
• Bias Term
• Gramian matrix
• Hashimoto et al.
• Linear classifiers by window training
• Yuta Kondo

• paper
• other

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