Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
This addresses the need for reliable uncertainty assessment in probabilistic models, which is incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of estimating uncertainty in probabilistic machine learning models by developing a systematic framework for epistemic and aleatoric uncertainty, using Gaussian Process Latent Variable Models and scalable approximations, with results illustrating the effectiveness in quantifying prediction confidence.
Uncertainty Quantification (UQ) is essential in probabilistic machine learning models, particularly for assessing the reliability of predictions. In this paper, we present a systematic framework for estimating both epistemic and aleatoric uncertainty in probabilistic models. We focus on Gaussian Process Latent Variable Models and employ scalable Random Fourier Features-based Gaussian Processes to approximate predictive distributions efficiently. We derive a theoretical formulation for UQ, propose a Monte Carlo sampling-based estimation method, and conduct experiments to evaluate the impact of uncertainty estimation. Our results provide insights into the sources of predictive uncertainty and illustrate the effectiveness of our approach in quantifying the confidence in the predictions.