5 citations · 7 across the 5 of their papers we have counts for
5 papers
Data splitting improves statistical performance in overparametrized regimes
Nicole Mücke, Enrico Reiss, Jonas Rungenhagen +1
While large training datasets generally offer improvement in model performance, the training process becomes computationally expensive and time consuming. Distributed learning is a…
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping
Nicole Mücke, Enrico Reiss
Stochastic Gradient Descent (SGD) has become the method of choice for solving a broad range of machine learning problems. However, some of its learning properties are still not ful…
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces
Ernesto De Vito, Nicole Mücke, Lorenzo Rosasco
We study reproducing kernel Hilbert spaces (RKHS) on a Riemannian manifold. In particular, we discuss under which condition Sobolev spaces are RKHS and characterize their reproduci…
Beating SGD Saturation with Tail-Averaging and Minibatching
Nicole Mücke, Gergely Neu, Lorenzo Rosasco
While stochastic gradient descent (SGD) is one of the major workhorses in machine learning, the learning properties of many practically used variants are poorly understood. In this…
Optimal Rates For Regularization Of Statistical Inverse Learning Problems
Gilles Blanchard, Nicole Mücke
We consider a statistical inverse learning problem, where we observe the image of a function through a linear operator at i.i.d. random design points , superposed with…