Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks

Autor:	Jimenez, Felix, Katzfuss, Matthias
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	For regression tasks, standard Gaussian processes (GPs) provide natural uncertainty quantification, while deep neural networks (DNNs) excel at representation learning. We propose to synergistically combine these two approaches in a hybrid method consisting of an ensemble of GPs built on the output of hidden layers of a DNN. GP scalability is achieved via Vecchia approximations that exploit nearest-neighbor conditional independence. The resulting deep Vecchia ensemble not only imbues the DNN with uncertainty quantification but can also provide more accurate and robust predictions. We demonstrate the utility of our model on several datasets and carry out experiments to understand the inner workings of the proposed method. Comment: 16 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.17063 Zobrazit plný text záznamu View this record from Arxiv