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Found 7 papers, 3 papers with code
Healing Gaussian Process Experts
Gaussian processes (GPs) are nonparametric Bayesian models that have been applied to regression and classification problems.
Understanding Deep Generative Models with Generalized Empirical Likelihoods
Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge.
Copula Flows for Synthetic Data Generation
Learning the probabilistic model for the data is equivalent to estimating the density of the data.
Gaussian Process Conditional Density Estimation
Conditional Density Estimation (CDE) models deal with estimating conditional distributions.
Orthogonally Decoupled Variational Gaussian Processes
It adopts an orthogonal basis in the mean function to model the residues that cannot be learned by the standard coupled approach.
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.
Expectation Propagation in Gaussian Process Dynamical Systems
Rich and complex time-series data, such as those generated from engineering sys- tems, financial markets, videos or neural recordings are now a common feature of modern data analysis.
