Comment: A Brownian Motion Model for Stochastic Simulation With Tunable Precision. - Rui Tuo; Peter Z. G. Qian; C. F. Jeff Wu (Technometrics, 2013)

Building Accurate Emulators for Stochastic Simulations via Quantile Kriging. - Matthew Plumlee; Rui Tuo (Technometrics, 2014)

Surrogate Modeling of Computer Experiments With Different Mesh Densities. - Rui Tuo; C. F. Jeff Wu; Dan Yu (Technometrics, 2014)

Sequential Exploration of Complex Surfaces Using Minimum Energy Designs. - V. Roshan Joseph; Tirthankar Dasgupta; Rui Tuo; C. F. Jeff Wu (Technometrics, 2015)

A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties. - Rui Tuo; C. F. Jeff Wu (SIAM/ASA J. Uncertain. Quantification, 2016)

Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion. - Xu He; Rui Tuo; C. F. Jeff Wu (Technometrics, 2017)

Differentially Private Change-Point Detection. - Rachel Cummings; Sara Krehbiel; Yajun Mei; Rui Tuo; Wanrong Zhang 001 (NeurIPS, 2018)

Stochastic Convergence of a Nonconforming Finite Element Method for the Thin Plate Spline Smoother for Observational Data. - Zhiming Chen; Rui Tuo; Wenlong Zhang (SIAM J. Numer. Anal., 2018)

Adjustments to Computer Models via Projected Kernel Calibration. - Rui Tuo (SIAM/ASA J. Uncertain. Quantification, 2019)

Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs. - V. Roshan Joseph; Dianpeng Wang; Li Gu; Shiji Lyu; Rui Tuo (Technometrics, 2019)

Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features. - Liang Ding 003; Rui Tuo; Shahin Shahrampour (ICML, 2020)

Projection Pursuit Gaussian Process Regression. - Gecheng Chen; Rui Tuo (CoRR, 2020)

Overcoming the Curse of Dimensionality in Density Estimation with Mixed Sobolev GANs. - Liang Ding 003; Rui Tuo; Shahin Shahrampour (CoRR, 2020)

Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs. - Rui Tuo; Wenjia Wang (J. Mach. Learn. Res., 2020)

On the Improved Rates of Convergence for Matérn-Type Kernel Ridge Regression with Application to Calibration of Computer Models. - Rui Tuo; Yan Wang 095; C. F. Jeff Wu (SIAM/ASA J. Uncertain. Quantification, 2020)

High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids. - Liang Ding 003; Rui Tuo; Xiaowei Zhang 004 (CoRR, 2021)

A Sparse Expansion For Deep Gaussian Processes. - Liang Ding 003; Rui Tuo; Shahin Shahrampour (CoRR, 2021)

Single and Multiple Change-Point Detection with Differential Privacy. - Wanrong Zhang 001; Sara Krehbiel; Rui Tuo; Yajun Mei; Rachel Cummings (J. Mach. Learn. Res., 2021)

Uncertainty Quantification for Bayesian Optimization. - Rui Tuo; Wenjia Wang (AISTATS, 2022)

Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations. - Haoyuan Chen; Liang Ding 003; Rui Tuo (J. Mach. Learn. Res., 2022)

Gaussian Process-Aided Function Comparison Using Noisy Scattered Data. - Abhinav Prakash; Rui Tuo; Yu Ding 002 (Technometrics, 2022)

Fitting and Peak Searching of Spectrum for Fourier Transform Infrared Spectrometer. - Wanjie Ren; Guojun Wang; Qi Liu 001; Xia Li; Rui Tuo (QRS Companion, 2023)

Privacy-aware Gaussian Process Regression. - Rui Tuo; Raktim Bhattacharya (CoRR, 2023)

The Temporal Overfitting Problem with Applications in Wind Power Curve Modeling. - Abhinav Prakash; Rui Tuo; Yu Ding 002 (Technometrics, 2023)

A General Theory for Kernel Packets: from state space model to compactly supported basis. - Liang Ding 003; Rui Tuo (CoRR, 2024)

Aggregation Models with Optimal Weights for Distributed Gaussian Processes. - Haoyuan Chen; Rui Tuo (CoRR, 2024)

Renewing Iterative Self-Labeling Domain Adaptation With Application to the Spine Motion Prediction. - Gecheng Chen; Yu Zhou; Xudong Zhang; Rui Tuo (IEEE Trans Autom. Sci. Eng., 2024)

From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation. - Wenyuan Zhao; Haoyuan Chen; Tie Liu 002; Rui Tuo; Chao Tian 002 (AISTATS, 2025)