Robust Deep Gaussian Processes

Understand

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.