3 papers
cs.LG2025
Improved Scaling Laws in Linear Regression via Data Reuse
Licong Lin, Jingfeng Wu, Peter L. Bartlett
Neural scaling laws suggest that the test error of large language models trained online decreases polynomially as the model size and data size increase. However, such scaling can b…
stat.ML2025
Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
Jingfeng Wu, Pierre Marion, Peter Bartlett
We study gradient descent (GD) with a constant stepsize for -regularized logistic regression with linearly separable data. Classical theory suggests small stepsizes to ensu…
stat.ML2025
Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes
Ruiqi Zhang, Jingfeng Wu, Licong Lin +1
We study (GD) for logistic regression on linearly separable data with stepsizes that adapt to the current risk, scaled by a constant hyperparameter .…