A0417
Title: Deep parametric predictive Gaussian processes for uncertainty estimation
Authors: Oluwole Oyebamiji - University of Birmingham (United Kingdom) [presenting]
Abstract: Deep Gaussian processes (DGPs) are a powerful extension of Gaussian processes that allow for multi-layer generalization of GPs, enabling more flexible and expressive modelling of complex data. However, as the depth of the model increases, so does the computational cost, making it challenging to scale deep Gaussian processes to large-dimensional data. This often leads to underestimation of the posterior variance. Moreover, interpreting and understanding the learned representations in DGPs can be more difficult than in shallower models. The model developed combines a hybrid spatial factor model that reduces the difficulty of dealing directly with high-dimensional outcomes and a Bayesian method that integrates input variability into GP regression. The proposed model used inducing point methods with stochastic variational inference, which provides substantially improved predictive uncertainties and efficient approximation. The benefits of the model are evaluated on several benchmark regression datasets and high-dimensional data from the IMPRESSIONS integrated assessment platform, version 2. The performance of the input features is analyzed using the proposed models and SHapley Additive exPlanations (SHAP) values for multi-task problems to help interpret the results. The results show that the proposed integration of these techniques is efficient and accurate for the uncertainty quantification of complex models.
