Robust Deep Gaussian Processes
Machine Learning
2019-05-22 v2 Artificial Intelligence
Machine Learning
Abstract
This report provides an in-depth overview over the implications and novelty Generalized Variational Inference (GVI) (Knoblauch et al., 2019) brings to Deep Gaussian Processes (DGPs) (Damianou & Lawrence, 2013). Specifically, robustness to model misspecification as well as principled alternatives for uncertainty quantification are motivated with an information-geometric view. These modifications have clear interpretations and can be implemented in less than 100 lines of Python code. Most importantly, the corresponding empirical results show that DGPs can greatly benefit from the presented enhancements.
Cite
@article{arxiv.1904.02303,
title = {Robust Deep Gaussian Processes},
author = {Jeremias Knoblauch},
journal= {arXiv preprint arXiv:1904.02303},
year = {2019}
}
Comments
11 pages, 4 figures