Search Results for author:
Found 9 papers, 0 papers with code
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
We present the first optimal rates for infinite-dimensional vector-valued ridge regression on a continuous scale of norms that interpolate between $L_2$ and the hypothesis space, which we consider as a vector-valued reproducing kernel Hilbert space.
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
However, we prove that, in terms of spectral properties and regularisation theory, this inverse problem is equivalent to the known compact inverse problem associated with scalar response regression.
Optimal Rates for Regularized Conditional Mean Embedding Learning
We address the misspecified setting, where the target CME is in the space of Hilbert-Schmidt operators acting from an input interpolation space between $\mathcal{H}_X$ and $L_2$, to $\mathcal{H}_Y$.
Nonparametric approximation of conditional expectation operators
This also provides a novel perspective on which limiting object the nonparametric estimate of $P$ converges to.
Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence
We consider autocovariance operators of a stationary stochastic process on a Polish space that is embedded into a reproducing kernel Hilbert space.
Kernel Conditional Density Operators
The proposed model is based on a novel approach to the reconstruction of probability densities from their kernel mean embeddings by drawing connections to estimation of Radon-Nikodym derivatives in the reproducing kernel Hilbert space (RKHS).
Kernel methods for detecting coherent structures in dynamical data
In particular, we show that kernel canonical correlation analysis (CCA) can be interpreted in terms of kernel transfer operators and that it can be obtained by optimizing the variational approach for Markov processes (VAMP) score.
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces
Reproducing kernel Hilbert spaces (RKHSs) play an important role in many statistics and machine learning applications ranging from support vector machines to Gaussian processes and kernel embeddings of distributions.
On Hyperparameter Search in Cluster Ensembles
Quality assessments of models in unsupervised learning and clustering verification in particular have been a long-standing problem in the machine learning research.
