86 citations · 120 across the 18 of their papers we have counts for
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cs.LG2024★ 1 cited
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…
cs.LG2024★ 1 cited
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 in…