Paul E. Chang | DeepAI

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∙ 06/06/2023

Memory-Based Dual Gaussian Processes for Sequential Learning

Sequential learning with Gaussian processes (GPs) is challenging when ac...

0 Paul E. Chang, et al. ∙

∙ 11/02/2022

Fantasizing with Dual GPs in Bayesian Optimization and Active Learning

Gaussian processes (GPs) are the main surrogate functions used for seque...

0 Paul E. Chang, et al. ∙

∙ 11/05/2021

Dual Parameterization of Sparse Variational Gaussian Processes

Sparse variational Gaussian process (SVGP) methods are a common choice f...

0 Vincent Adam, et al. ∙

∙ 07/12/2020

State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

We formulate approximate Bayesian inference in non-conjugate temporal an...

0 William J. Wilkinson, et al. ∙

∙ 07/09/2020

Fast Variational Learning in State-Space Gaussian Process Models

Gaussian process (GP) regression with 1D inputs can often be performed i...

0 Paul E. Chang, et al. ∙

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