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Found 6 papers, 3 papers with code
Nyström $M$-Hilbert-Schmidt Independence Criterion
In order to alleviate the quadratic computational bottleneck in large-scale applications, multiple HSIC approximations have been proposed, however these estimators are restricted to $M=2$ random variables, do not extend naturally to the $M\ge 2$ case, and lack theoretical guarantees.
Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'
We discuss how MultiFIT, the Multiscale Fisher's Independence Test for Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing linear-time kernel tests based on the Hilbert-Schmidt independence criterion (HSIC).
Kernel Minimum Divergence Portfolios
Portfolio optimization is a key challenge in finance with the aim of creating portfolios matching the investors' preference.
Emotion Transfer Using Vector-Valued Infinite Task Learning
Style transfer is a significant problem of machine learning with numerous successful applications.
Infinite-Task Learning with RKHSs
A step further consists of learning a continuum of tasks for various loss functions.
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output.
