19 citations · 87 across the 19 of their papers we have counts for
5 papers · 1 filter
Bayesian Deconditional Kernel Mean Embeddings
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…
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference
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 param…
Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds
Kelvin Hsu, Richard Nock, Fabio Ramos
Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space. While they provide a flexible and powerful f…
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
Louis C. Tiao, Edwin V. Bonilla, Fabio Ramos
We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlyin…
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels
Anthony Tompkins, Fabio Ramos
Periodicity is often studied in timeseries modelling with autoregressive methods but is less popular in the kernel literature, particularly for higher dimensional problems such as…