# Variational Outlier-Robust Gaussian Process Regression with Generative Modeling

arXiv: https://arxiv.org/abs/2608.16606 · HTML: https://mlsanity.com/paper/2608.16606/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection](https://mlsanity.com/paper/1909.08605/) (2019)
- [Robust and Conjugate Gaussian Process Regression](https://mlsanity.com/paper/2311.00463/) (2023)
- [Outlier-Robust Filtering For Nonlinear Systems With Selective Observations Rejection](https://mlsanity.com/paper/2106.05706/) (2021)
- [Bayesian Heuristics for Robust Spatial Perception](https://mlsanity.com/paper/2212.00344/) (2022)
- [EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise](https://mlsanity.com/paper/2509.07415/) (2025)

## Abstract (arXiv)

Outliers can substantially distort Gaussian process regression (GPR) due to its conventional Gaussian observation likelihood, leading to inaccurate model learning and prediction. To address this limitation, this article introduces a generative GPR model that captures observation-specific contamination and adaptively mitigates the influence of outliers. Subsequently, a variational generalized expectation-maximization procedure is used to learn the latent variables and GPR model parameters. Experiments on synthetic and real datasets under different contamination settings demonstrate that the proposed method remains competitive with-and in several cases outperforms-robust GPR baselines in prediction accuracy. Moreover, the proposed method shares the cubic computational scaling of the compared GPR methods.