38 citations · 87 across the 35 of their papers we have counts for
7 papers · 1 filter
The temporal overfitting problem with applications in wind power curve modeling
Abhinav Prakash, Rui Tuo, Yu Ding
This paper is concerned with a nonparametric regression problem in which the input variables and the errors are autocorrelated in time. The motivation for the research stems from m…
High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces
Liang Ding, Lu Zou, Wenjia Wang +2
Density estimation plays a key role in many tasks in machine learning, statistical inference, and visualization. The main bottleneck in high-dimensional density estimation is the p…
Projection Pursuit Gaussian Process Regression
Gecheng Chen, Rui Tuo
A primary goal of computer experiments is to reconstruct the function given by the computer code via scattered evaluations. Traditional isotropic Gaussian process models suffer fro…
Gaussian process aided function comparison using noisy scattered data
Abhinav Prakash, Rui Tuo, Yu Ding
This work proposes a nonparametric method to compare the underlying mean functions given two noisy datasets. The motivation for the work stems from an application of comparing wind…
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Liang Ding, Rui Tuo, Shahin Shahrampour
Despite their success, kernel methods suffer from a massive computational cost in practice. In this paper, in lieu of commonly used kernel expansion with respect to inputs, we…
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…
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…