In this paper, we extend this criterion to deal with with structured and interdependent observations. This is achieved by modeling the structures using ...
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Many machine learning algorithms can be formulated in the framework of statis- tical independence such as the Hilbert Schmidt Independence Criterion.
Many machine learning algorithms can be formulated in the framework of statis- tical independence such as the Hilbert Schmidt Independence Criterion.
In this paper, we extend this criterion to deal with structured and interdependent observations. This is achieved by modeling the structures using undirected ...
In this paper, we extend this criterion to deal with structured and interdependent obser- vations. This is achieved by modeling the structures using undirected ...
Dec 8, 2008 · In this paper, we extend this criterion to deal with structured and interdependent observations. This is achieved by modeling the structures ...
Oct 1, 2020 · Two-sample and independence tests with the kernel-based MMD and HSIC have shown remarkable results on i.i.d. data and stationary random ...
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables.
We propose a new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces.
Kernel-based tests of independence have gained popularity to deal with nonlinear dependencies in recent years, but testing for conditional independence remains ...