267 citations · 813 across the 34 of their papers we have counts for
39 papers
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
Marvin Pförtner, Ingo Steinwart, Philipp Hennig +1
Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, an…
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning
Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart +1
Pruning neural networks reduces inference time and memory costs. On standard hardware, these benefits will be especially prominent if coarse-grained structures, like feature maps,…
Intrinsic Dimension Adaptive Partitioning for Kernel Methods
Thomas Hamm, Ingo Steinwart
We prove minimax optimal learning rates for kernel ridge regression, resp.~support vector machines based on a data dependent partition of the input space, where the dependence of t…
Which Minimizer Does My Neural Network Converge To?
Manuel Nonnenmacher, David Reeb, Ingo Steinwart
The loss surface of an overparameterized neural network (NN) possesses many global minima of zero training error. We explain how common variants of the standard NN training procedu…
Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space
Ingo Steinwart
Given an uncountable, compact metric space, we show that there exists no reproducing kernel Hilbert space that contains the space of all continuous functions on this compact space.
Adaptive Learning Rates for Support Vector Machines Working on Data with Low Intrinsic Dimension
Thomas Hamm, Ingo Steinwart
We derive improved regression and classification rates for support vector machines using Gaussian kernels under the assumption that the data has some low-dimensional intrinsic stru…