most citedRisk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization

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Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization

Jingfeng Wu, Peter L. Bartlett, Sham M. Kakade +2

Existing theory suggests that for linear regression problems categorized by capacity and source conditions, gradient descent (GD) is always minimax optimal, while both ridge regres…

stat.ML2026

Hard labels sampled from sparse targets mislead rotation invariant algorithms

Avrajit Ghosh, Bin Yu, Manfred Warmuth +1

One of the most common machine learning setups is logistic regression. In many classification models, including neural networks, the final prediction is obtained by applying a logi…