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Found 5 papers, 2 papers with code
Adaptive Inducing Points Selection For Gaussian Processes
Gaussian Processes (\textbf{GPs}) are flexible non-parametric models with strong probabilistic interpretation.
Evidence Estimation by Kullback-Leibler Integration for Flow-Based Methods
Flow-based methods such as Stein Variational Gradient Descent caught a lot of interest due to their flexibility and the strong theory going with them.
Gaussian Density Parametrization Flow: Particle and Stochastic Approaches
Bayesian inference is intractable for most practical problems and requires approximation schemes with several trade-offs.
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
Building on the conjugate structure of the augmented model, we develop two inference methods.
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function.
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