# Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning

arXiv: https://arxiv.org/abs/2210.10964 · HTML: https://mlsanity.com/paper/2210.10964/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Deep Kernel Learning](https://mlsanity.com/paper/1511.02222/) (2015)
- [A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement](https://mlsanity.com/paper/2204.09308/) (2022)
- [Non-Stationary Spectral Kernels](https://mlsanity.com/paper/1705.08736/) (2017)
- [Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo](https://mlsanity.com/paper/1508.04319/) (2015)
- [Deep Gaussian Processes: A Survey](https://mlsanity.com/paper/2106.12135/) (2021)
- [Approximate Inference for Nonstationary Heteroscedastic Gaussian process Regression](https://mlsanity.com/paper/1404.5443/) (2014)
- [Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning](https://mlsanity.com/paper/2010.04315/) (2020)

## Built on by (most cited first, 2 of 2)

- [Environmental Sensor Placement with Convolutional Gaussian Neural Processes](https://mlsanity.com/paper/2211.10381/) (2022)
- [Dealing with Uncertainty in Contextual Anomaly Detection](https://mlsanity.com/paper/2507.04490/) (2025)

## Abstract (arXiv)

Gaussian processes are Bayesian non-parametric models used in many areas. In this work, we propose a Non-stationary Heteroscedastic Gaussian process model which can be learned with gradient-based techniques. We demonstrate the interpretability of the proposed model by separating the overall uncertainty into aleatoric (irreducible) and epistemic (model) uncertainty. We illustrate the usability of derived epistemic uncertainty on active learning problems. We demonstrate the efficacy of our model with various ablations on multiple datasets.