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Found 7 papers, 2 papers with code
Additive Gaussian Processes Revisited
Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power.
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
In the univariate setting, using the kernel spectral representation is an appealing approach for generating stationary covariance functions.
Enriched Mixtures of Gaussian Process Experts
Mixtures of experts probabilistically divide the input space into regions, where the assumptions of each expert, or conditional model, need only hold locally.
Adaptive Sensor Placement for Continuous Spaces
We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events.
Decision Variance in Online Learning
In this paper, a risk-averse online learning problem under the performance measure of the mean-variance of the rewards is studied.
GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end.
Simple approximate MAP Inference for Dirichlet processes
This is a well-posed approximation to the MAP solution of the probabilistic DPM model.
