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How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent
We analyze the generalization properties of two-layer neural networks in the neural tangent kernel (NTK) regime, trained with gradient descent (GD).
Random feature approximation for general spectral methods
Random feature approximation is arguably one of the most popular techniques to speed up kernel methods in large scale algorithms and provides a theoretical approach to the analysis of deep neural networks.
Local SGD in Overparameterized Linear Regression
We consider distributed learning using constant stepsize SGD (DSGD) over several devices, each sending a final model update to a central server.
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