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Found 11 papers, 4 papers with code
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
In this work we propose an algorithm for sparse Gaussian process regression which leverages MCMC to sample from the hyperparameter posterior within the variational inducing point framework of Titsias (2009).
The Promises and Pitfalls of Deep Kernel Learning
Through careful experimentation on the UCI, CIFAR-10, and the UTKFace datasets, we find that the overfitting from overparameterized maximum marginal likelihood, in which the model is "somewhat Bayesian", can in certain scenarios be worse than that from not being Bayesian at all.
Deep Structured Mixtures of Gaussian Processes
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs.
Rates of Convergence for Sparse Variational Gaussian Process Regression
Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$.
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
In this paper, we introduce a natural and expressive way to tackle these problems, by incorporating GPs in sum-product networks (SPNs), a recently proposed tractable probabilistic model allowing exact and efficient inference.
Variational Gaussian Process State-Space Models
State-space models have been successfully used for more than fifty years in different areas of science and engineering.
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
We show that GP performance improves with increasing amounts of data in regression (on flight data with 2 million records) and latent variable modelling (on MNIST).
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems.
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems.
Active Learning of Model Evidence Using Bayesian Quadrature
Numerical integration is an key component of many problems in scientific computing, statistical modelling, and machine learning.
Gaussian Process Training with Input Noise
This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean.
