Search Results for author: Kelvin Hsu

Found 3 papers, 1 papers with code

Bayesian Deconditional Kernel Mean Embeddings

no code implementations1 Jun 2019 Kelvin Hsu, Fabio Ramos

Conditional kernel mean embeddings form an attractive nonparametric framework for representing conditional means of functions, describing the observation processes for many complex models.

Gaussian Processes

Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference

no code implementations3 Mar 2019 Kelvin Hsu, Fabio Ramos

In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations.

Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds

1 code implementation1 Sep 2018 Kelvin Hsu, Richard Nock, Fabio Ramos

Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space.

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