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Gaussian Process Regression Networks
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We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.
Forward citations
Cited by 2 Pith papers
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Convolution-Based Converter : A Weak-Prior Approach For Modeling Stochastic Processes Based On Conditional Density Estimation
A convolutional noise-to-trajectory network trained with only an observation-matching loss is claimed to estimate conditional distributions of stochastic processes without strong priors.
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Derivation of Output Correlation Inferences for Multi-Output (aka Multi-Task) Gaussian Process
A tutorial that re-derives the known EM and gradient formulas for multi-task Gaussian processes, with two mathematical errors in the presented derivations.
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