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Activity Number: 497 - ENVR Student Paper Awards
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #301780 Presentation
Title: Adaptive Ensemble Learning for Spatiotemporal Processes with Calibrated Predictive Uncertainty: a Bayesian Nonparametric Approach
Author(s): Jeremiah Liu*
Keywords: Ensemble learning; Uncertainty quantification; Calibration; Gaussian process; Spatiotemporal modeling; Air pollution

Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assign to base models a set of deterministic, constant model weights that (1) do not fully account for individual models’ varying accuracy across data subgroups, nor (2) provide uncertainty estimates for the ensemble prediction, which could result in mis-calibrated (i.e. precise but biased) predictions that could in turn negatively impact the algorithm performance in real-word applications. In this work, we present an adaptive, probabilistic approach to ensemble learning using a transformed Gaussian process as a prior for the ensemble weights. Given input feature, our method optimally combines base models based on their predictive accuracy in the feature space, and provides interpretable uncertainty estimates both in model selection and in ensemble prediction. We illustrate the utility of our method on the real-world application of spatiotemporal integration of particle pollution prediction models in greater Boston region.

Authors who are presenting talks have a * after their name.

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