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

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cs.LG2025

Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression

Jingfeng Wu, Peter Bartlett, Matus Telgarsky +1

In overparameterized logistic regression, gradient descent (GD) iterates diverge in norm while converging in direction to the maximum -margin solution -- a phenomenon known…

cs.LG2024

Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency

Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky +1

We consider gradient descent (GD) with a constant stepsize applied to logistic regression with linearly separable data, where the constant stepsize is so large that the loss i…