activity
20152026
most citedDeterministic Sampling of Expensive Posteriors Using Minimum Energy Designs

38 citations · 87 across the 35 of their papers we have counts for

collaborators
Showing math.STShow all

7 papers · 1 filter

math.ST2024

Asymptotic Theory for Linear Functionals of Kernel Ridge Regression

Rui Tuo, Lu Zou

An asymptotic theory is established for linear functionals of the predictive function given by kernel ridge regression, when the reproducing kernel Hilbert space is equivalent to a…

math.ST2020★ 2 cited

Uncertainty Quantification for Bayesian Optimization

Rui Tuo, Wenjia Wang

Bayesian optimization is a class of global optimization techniques. In Bayesian optimization, the underlying objective function is modeled as a realization of a Gaussian process. A…

math.ST2020★ 1 cited

On the Improved Rates of Convergence for Matérn-type Kernel Ridge Regression, with Application to Calibration of Computer Models

Rui Tuo, Yan Wang, C. F. Jeff Wu

Kernel ridge regression is an important nonparametric method for estimating smooth functions. We introduce a new set of conditions, under which the actual rates of convergence of t…

math.ST2019★ 3 cited

Kriging prediction with isotropic Matérn correlations: Robustness and experimental design

Rui Tuo, Wenjia Wang

This work investigates the prediction performance of the kriging predictors. We derive some error bounds for the prediction error in terms of non-asymptotic probability under the u…

math.ST2018★ 15 cited

Differentially Private Change-Point Detection

Rachel Cummings, Sara Krehbiel, Yajun Mei +2

The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data. This problem appears in many important practical sett…

math.ST2017

On Prediction Properties of Kriging: Uniform Error Bounds and Robustness

Wenjia Wang, Rui Tuo, C. F. Jeff Wu

Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simp…

math.ST2017★ 1 cited

Prediction based on the Kennedy-O'Hagan calibration model: asymptotic consistency and other properties

Rui Tuo, C. F. Jeff Wu

Kennedy and O'Hagan (2001) propose a model for calibrating some unknown parameters in a computer model and estimating the discrepancy between the computer output and physical respo…