Gaussian processes improved for better predictions when models are imperfect
Predictively Oriented Gaussian Process Posteriors
Machine Learning
Summary
Gaussian Processes are tools that help predict outcomes and understand uncertainty, but they need carefully chosen settings to work well. The paper introduces Predictively Oriented Gaussian Processes (PrO-GPs), which focus on making predictions more reliable even if the model setup isn't perfect. The authors created a way to compute these predictions efficiently and showed that their method better matches real uncertainties compared to traditional approaches. This helps make predictions more trustworthy when modeling real-world data.
What this means in practice
- •For machine learning engineers: Improve prediction reliability in models despite imperfect assumptions or data conditions.
- •For financial risk analysts: Generate better-calibrated uncertainty estimates in financial forecasting models under uncertainty.
Authors
Callum Lau, Jeremias Knoblauch, Louis Sharrock
Abstract
Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioners to make a number of design decisions, such as the choice of the kernel and the observation model. Suboptimal choices can produce misspecified models that do not capture the underlying data generating process. We introduce Predictively Oriented Gaussian Processes (PrO-GPs), which treat predictive uncertainty as the primary inferential target and provide a robust alternative to standard GPs. Although direct computation of a PrO posterior for nonparametric models is intractable, we derive a reduced formulation and practical sampling scheme for efficient computation. Through synthetic and real data experiments, we show that PrO-GPs produce better calibrated predictive distributions under model misspecification compared to standard GP approaches.