Variational Outlier-Robust Gaussian Process Regression with Generative Modeling
2026-08-17 • Machine Learning
Machine Learning
AI summaryⓘ
The authors explain that traditional Gaussian process regression (GPR) struggles when data has outliers, which are unusual points that can mess up the model. To fix this, they created a new model that notices when data points might be outliers and reduces their effect. They use a special learning method to train this model, and tests show it works well compared to other robust methods. Importantly, their method still works efficiently with similar computing speed as standard GPR.
Gaussian Process RegressionOutliersGenerative ModelVariational InferenceExpectation-MaximizationRobust StatisticsLatent VariablesPrediction AccuracyComputational ComplexityModel Contamination
Authors
Arslan Majal, Aamir Hussain Chughtai
Abstract
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.