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…
cs.LG2025
Scaling Laws in Linear Regression: Compute, Parameters, and Data
Licong Lin, Jingfeng Wu, Sham M. Kakade +2
Empirically, large-scale deep learning models often satisfy a neural scaling law: the test error of the trained model improves polynomially as the model size and data size grow. Ho…
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 …