# Self-explaining variational posterior distributions for Gaussian Process models

arXiv: https://arxiv.org/abs/2109.03708 · HTML: https://mlsanity.com/paper/2109.03708/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Scalable Variational Gaussian Process Classification](https://mlsanity.com/paper/1411.2005/) (2014)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Functional Variational Bayesian Neural Networks](https://mlsanity.com/paper/1903.05779/) (2019)
- [Generalized Variational Inference: Three arguments for deriving new Posteriors](https://mlsanity.com/paper/1904.02063/) (2019)
- [Don't Unroll Adjoint: Differentiating SSA-Form Programs](https://mlsanity.com/paper/1810.07951/) (2018)
- [Understanding Variational Inference in Function-Space](https://mlsanity.com/paper/2011.09421/) (2020)
- [Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior](https://mlsanity.com/paper/2006.00783/) (2020)
- [A Quantitative Perspective on Values of Domain Knowledge for Machine Learning](https://mlsanity.com/paper/2011.08450/) (2020)

## Abstract (arXiv)

Bayesian methods have become a popular way to incorporate prior knowledge and a notion of uncertainty into machine learning models. At the same time, the complexity of modern machine learning makes it challenging to comprehend a model's reasoning process, let alone express specific prior assumptions in a rigorous manner. While primarily interested in the former issue, recent developments intransparent machine learning could also broaden the range of prior information that we can provide to complex Bayesian models. Inspired by the idea of self-explaining models, we introduce a corresponding concept for variational GaussianProcesses. On the one hand, our contribution improves transparency for these types of models. More importantly though, our proposed self-explaining variational posterior distribution allows to incorporate both general prior knowledge about a target function as a whole and prior knowledge about the contribution of individual features.