Search Results for author:
Found 5 papers, 0 papers with code
Convergence analysis of online algorithms for vector-valued kernel regression
We consider the problem of approximating the regression function from noisy vector-valued data by an online learning algorithm using an appropriate reproducing kernel Hilbert space (RKHS) as prior.
Deep Neural Networks and PIDE discretizations
In this paper, we propose neural networks that tackle the problems of stability and field-of-view of a Convolutional Neural Network (CNN).
Sparse tensor product approximation for a class of generalized method of moments estimators
Motivated by the popular Probit and Mixed Logit models, we consider double integrals with a linking function which stems from the considered estimator, e. g. the logarithm for Maximum Likelihood, and apply a sparse tensor product quadrature to reduce the computational effort for the approximation of the combined integral.
Methodology Numerical Analysis Numerical Analysis 62P20, 65D30, 65D32
Optimally rotated coordinate systems for adaptive least-squares regression on sparse grids
In this paper we propose a preprocessing approach for these adaptive sparse grid algorithms that determines an optimized, problem-dependent coordinate system and, thus, reduces the effective dimensionality of a given data set in the ANOVA sense.
A representer theorem for deep kernel learning
In this paper we provide a finite-sample and an infinite-sample representer theorem for the concatenation of (linear combinations of) kernel functions of reproducing kernel Hilbert spaces.
