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Found 4 papers, 2 papers with code
Inferring Manifolds From Noisy Data Using Gaussian Processes
In analyzing complex datasets, it is often of interest to infer lower dimensional structure underlying the higher dimensional observations.
Graph Based Gaussian Processes on Restricted Domains
The GL is constructed from a kernel which depends only on the Euclidean coordinates of the inputs.
Estimating densities with nonlinear support using Fisher-Gaussian kernels
We provide theory on large support, and illustrate gains relative to competitors in simulated and real data applications.
Methodology Statistics Theory Statistics Theory
Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent states
We propose a targeted sub-sampling (TASS) approach that over-samples observations corresponding to rare latent states when calculating the stochastic gradient of parameters associated with them.
