Sequential sampling for Bayesian robust ranking and selection. - Xiaowei Zhang 004; Liang Ding 003 (WSC, 2016)

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

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

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)

Representing Additive Gaussian Processes by Sparse Matrices. - Lu Zou; Haoyuan Chen; Liang Ding 003 (CoRR, 2023)

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

Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression. - Lu Zou; Liang Ding 003 (CoRR, 2024)

Sample and Computationally Efficient Stochastic Kriging in High Dimensions. - Liang Ding 003; Xiaowei Zhang 004 (Oper. Res., 2024)