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05/25/2023
Privacy-aware Gaussian Process Regression
We propose the first theoretical and methodological framework for Gaussi...
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11/14/2022
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction
The area of transfer learning comprises supervised machine learning meth...
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05/06/2022
Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes
This work is concerned with providing a principled decision process for ...
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03/07/2022
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
We develop an exact and scalable algorithm for one-dimensional Gaussian ...
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12/11/2021
A Sparse Expansion For Deep Gaussian Processes
Deep Gaussian Processes (DGP) enable a non-parametric approach to quanti...
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07/19/2021
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
High-dimensional simulation optimization is notoriously challenging. We ...
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07/17/2021
A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models
This paper develops a frequentist solution to the functional calibration...
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12/02/2020
The temporal overfitting problem with applications in wind power curve modeling
This paper is concerned with a nonparametric regression problem in which...
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06/05/2020
Overcoming the Curse of Dimensionality in Density Estimation with Mixed Sobolev GANs
We propose a novel GAN framework for non-parametric density estimation w...
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04/01/2020
Projection Pursuit Gaussian Process Regression
A primary goal of computer experiments is to reconstruct the function gi...
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03/17/2020
Gaussian process aided function comparison using noisy scattered data
This work proposes a new nonparametric method to compare the underlying ...
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02/11/2020
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Despite their success, kernel methods suffer from a massive computationa...
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02/04/2020
Uncertainty Quantification for Bayesian Optimization
Bayesian optimization is a class of global optimization techniques. It r...
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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...
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12/29/2019
Effective Model Calibration via Sensible Variable Identification and Adjustment, with Application to Composite Fuselage Simulation
Estimation of model parameters of computer simulators, also known as cal...
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11/13/2019
Kriging prediction with isotropic Matérn correlations: robustness and experimental design
We investigate the prediction performance of the kriging predictors. We ...
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08/29/2018
Differentially Private Change-Point Detection
The change-point detection problem seeks to identify distributional chan...
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12/24/2017
Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs
Markov chain Monte Carlo (MCMC) methods require a large number of sample...
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10/18/2017