6 citations · 6 across the 2 of their papers we have counts for
3 papers · 1 filter
Preconditioned Additive Gaussian Processes with Fourier Acceleration
Theresa Wagner, Tianshi Xu, Franziska Nestler +2
Gaussian processes (GPs) are crucial in machine learning for quantifying uncertainty in predictions. However, their associated covariance matrices, defined by kernel functions, are…
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel Derivatives
Theresa Wagner, Franziska Nestler, Martin Stoll
One of the main computational bottlenecks when working with kernel based learning is dealing with the large and typically dense kernel matrix. Techniques dealing with fast approxim…
Learning in High-Dimensional Feature Spaces Using ANOVA-Based Fast Matrix-Vector Multiplication
Franziska Nestler, Martin Stoll, Theresa Wagner
Kernel matrices are crucial in many learning tasks such as support vector machines or kernel ridge regression. The kernel matrix is typically dense and large-scale. Depending on th…