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11/04/2022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
The kernel function and its hyperparameters are the central model select...
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02/24/2021
The Promises and Pitfalls of Deep Kernel Learning
Deep kernel learning and related techniques promise to combine the repre...
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10/10/2019
Deep Structured Mixtures of Gaussian Processes
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression...
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03/08/2019
Rates of Convergence for Sparse Variational Gaussian Process Regression
Excellent variational approximations to Gaussian process posteriors have...
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09/12/2018
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
While Gaussian processes (GPs) are the method of choice for regression t...
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02/06/2014
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Gaussian processes (GPs) are a powerful tool for probabilistic inference...
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12/17/2013
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
Gaussian process state-space models (GP-SSMs) are a very flexible family...
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06/12/2013