research
∙
06/13/2023
ACE: Active Learning for Causal Inference with Expensive Experiments
Experiments are the gold standard for causal inference. In many applicat...
share
research
∙
09/27/2022
Multi-Stage Multi-Fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions
In an era where scientific experimentation is often costly, multi-fideli...
research
∙
01/17/2021
TSEC: a framework for online experimentation under experimental constraints
Thompson sampling is a popular algorithm for solving multi-armed bandit ...
research
∙
12/22/2020
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations
Kriging (or Gaussian process regression) is a popular machine learning m...
research
∙
02/04/2020
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process
In this paper, we investigate Gaussian process regression with input loc...
research
∙
01/19/2020
Finding Optimal Points for Expensive Functions Using Adaptive RBF-Based Surrogate Model Via Uncertainty Quantification
Global optimization of expensive functions has important applications in...
research
∙
01/01/2020
On the Improved Rates of Convergence for Matérn-type Kernel Ridge Regression, with Application to Calibration of Computer Models
Kernel ridge regression is an important nonparametric method for estimat...
research
∙
11/17/2019
A hierarchical expected improvement method for Bayesian optimization
Expected improvement (EI) is one of the most popular Bayesian optimizati...
research
∙
08/23/2019
BdryGP: a new Gaussian process model for incorporating boundary information
Gaussian processes (GPs) are widely used as surrogate models for emulati...
research
∙
06/05/2018
Calibration for computer experiments with binary responses
Calibration refers to the estimation of unknown parameters which are pre...
research
∙
02/24/2018
Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation for prediction of spatiotemporally evolving flow dynamics
This interdisciplinary study, which combines machine learning, statistic...
research
∙
12/10/2017
Analysis-of-marginal-Tail-Means - a new method for robust parameter optimization
This paper presents a novel method, called Analysis-of-marginal-Tail-Mea...
research
∙
10/18/2017
Universal Convergence of Kriging
Kriging based on Gaussian random fields is widely used in reconstructing...
research
∙
07/26/2017
