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Oct, 2018
通过最小化PAC-Bayesian广义化界限来学习高斯过程
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
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David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch
TL;DR
该研究提出了一种使用PAC-Bayesian边界来直接优化GPs及其稀疏逼近的方法，相比于最大化边缘似然的常规方法，该方法具有更好的稳健性和泛化性能，并在多个回归基准数据集上获得了显着的泛化保证。
Abstract
gaussian processes
(GPs) are a generic modelling tool for
supervised learning
. While they have been successfully applied on large datasets, their use in safety-critical applications is hindered by the lack of goo
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