38 citations · 87 across the 35 of their papers we have counts for
36 papers
SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
Wenyuan Zhao, Rui Tuo, Chao Tian
Gaussian processes (GPs) provide a principled Bayesian framework for uncertainty estimation, but their computational complexity severely limits scalability to large datasets. We pr…
Statistical Validation of Computer Models: Global and Subdomain Hypothesis Testing
Chaoan Li, Xianyang Zhang, Rui Tuo
Computer simulations play an important role in scientific discovery and engineering innovation. Reliable computer models enable virtual experimentation that reduces the need for co…
BI-DCGAN: A Theoretically Grounded Bayesian Framework for Efficient and Diverse GANs
Mahsa Valizadeh, Rui Tuo, James Caverlee
Generative Adversarial Networks (GANs) are proficient at generating synthetic data but continue to suffer from mode collapse, where the generator produces a narrow range of outputs…
Language Models as Semantic Augmenters for Sequential Recommenders
Mahsa Valizadeh, Xiangjue Dong, Rui Tuo +1
Large Language Models (LLMs) excel at capturing latent semantics and contextual relationships across diverse modalities. However, in modeling user behavior from sequential interact…
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
Wenyuan Zhao, Haoyuan Chen, Tie Liu +2
With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained considerable attention in recent years. From the co…
Inverse Design of Nonlinear Mechanics of Bio-inspired Materials Through Interface Engineering and Bayesian Optimization
Wei Zhang, Mingjian Tang, Haoxuan Mu +6
In many biological materials such as nacre and bone, the material structure consists of hard grains and soft interfaces, with the interfaces playing a significant role in the mater…
Aggregation Models with Optimal Weights for Distributed Gaussian Processes
Haoyuan Chen, Rui Tuo
Gaussian process (GP) models have received increasing attention in recent years due to their superb prediction accuracy and modeling flexibility. To address the computational burde…
Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner
Haoyuan Chen, Rui Tuo
Gaussian processes (GPs) are widely used in non-parametric Bayesian modeling, and play an important role in various statistical and machine learning applications. In a variety task…
Fixed-budget optimal designs for multi-fidelity computer experiments
Gecheng Chen, Rui Tuo
This work focuses on the design of experiments of multi-fidelity computer experiments. We consider the autoregressive Gaussian process model proposed by Kennedy and O'Hagan (2000)…
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…
Beyond State Space Representation: A General Theory for Kernel Packets
Liang Ding, Rui Tuo, Lu Zhou
Gaussian process (GP) regression provides a flexible, nonparametric framework for probabilistic modeling, yet remains computationally demanding in large-scale applications. For one…
Privacy-aware Gaussian Process Regression
Rui Tuo, Haoyuan Chen, Raktim Bhattacharya
We propose a novel theoretical and methodological framework for Gaussian process regression subject to privacy constraints. The proposed method can be used when a data owner is unw…
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction
Gecheng Chen, Yu Zhou, Xudong Zhang +1
The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distr…
Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes
Shilan Jin, Rui Tuo, Akash Tiwari +5
This work is concerned with providing a principled decision process for stopping or tool-changing in a surface finishing process. The decision process is supposed to work for produ…
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
Haoyuan Chen, Liang Ding, Rui Tuo
We develop an exact and scalable algorithm for one-dimensional Gaussian process regression with Matérn correlations whose smoothness parameter is a half-integer. The proposed a…
A Sparse Expansion For Deep Gaussian Processes
Liang Ding, Rui Tuo, Shahin Shahrampour
In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models c…
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
Liang Ding, Rui Tuo, Xiaowei Zhang
High-dimensional simulation optimization is notoriously challenging. We propose a new sampling algorithm that converges to a global optimal solution and suffers minimally from the…
A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models
Rui Tuo, Shiyuan He, Arash Pourhabib +2
This paper develops a frequentist solution to the functional calibration problem, where the value of a calibration parameter in a computer model is allowed to vary with the value o…
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…