1 citations · 2 across the 7 of their papers we have counts for
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Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators
Mike Nguyen, Nicole Mücke
In this work, we investigate the generalization properties of random feature methods. Our analysis extends prior results for Tikhonov regularization to a broad class of spectral re…
Random feature approximation for general spectral methods
Mike Nguyen, Nicole Mücke
Random feature approximation is arguably one of the most widely used techniques for kernel methods in large-scale learning algorithms. In this work, we analyze the generalization p…
Optimal Convergence Rates for Neural Operators
Mike Nguyen, Nicole Mücke
We introduce the neural tangent kernel (NTK) regime for two-layer neural operators and analyze their generalization properties. For early-stopped gradient descent (GD), we derive f…
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent
Mike Nguyen, Nicole Mücke
We analyze the generalization properties of two-layer neural networks in the neural tangent kernel (NTK) regime, trained with gradient descent (GD). For early stopped GD we derive…
Local SGD in Overparameterized Linear Regression
Mike Nguyen, Charly Kirst, Nicole Mücke
We consider distributed learning using constant stepsize SGD (DSGD) over several devices, each sending a final model update to a central server. In a final step, the local estimate…