Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

no code implementations • NeurIPS 2019 • Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull

We introduce a Bayesian nonparametric ensemble (BNE) approach that augments an existing ensemble model to account for different sources of model uncertainty.

Bias Detection Ensemble Learning +1

Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes

no code implementations • NeurIPS 2017 • Jeremiah Zhe Liu, Brent Coull

Utilizing the theory of reproducing kernels, we reduce this hypothesis to a simple one-sided score test for a scalar parameter, develop a testing procedure that is robust against the mis-specification of kernel functions, and also propose an ensemble-based estimator for the null model to guarantee test performance in small samples.

Gaussian Processes