1.7k citations · 1.9k across the 10 of their papers we have counts for
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Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon
Jeremiah Zhe Liu
This work develops rigorous theoretical basis for the fact that deep Bayesian neural network (BNN) is an effective tool for high-dimensional variable selection with rigorous uncert…
A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies
Jeremiah Zhe Liu, Jane Lee, Pi-i Debby Lin +6
Gene-environment and nutrition-environment studies often involve testing of high-dimensional interactions between two sets of variables, each having potentially complex nonlinear m…
Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees
Jeremiah Zhe Liu
We introduce constrained Gaussian process (CGP), a Gaussian process model for random functions that allows easy placement of mathematical constrains (e.g., non-negativity, monotoni…
Spatially Adaptive Ensemble Learning with Calibrated Predictive Uncertainty
Yanran Li, Jeremiah Zhe Liu, John Paisley +2
Air pollution exposure assessment often uses ensembles of spatio-temporal models, but a critical limitation is that conventional methods use deterministic weights and fail to quant…