Phase transition of graph Laplacian of high dimensional noisy random point cloud. - Xiucai Ding; Hau-Tieng Wu (CoRR, 2020)

ASSVD: Adaptive Sparse Singular Value Decomposition for High Dimensional Matrices. - Xiucai Ding; Xianyi Chen; Mengling Zou; Guangxing Zhang (KSII Trans. Internet Inf. Syst., 2020)

The conjugate gradient algorithm on a general class of spiked covariance matrices. - Xiucai Ding; Thomas Trogdon (CoRR, 2021)

How do kernel-based sensor fusion algorithms behave under high dimensional noise? - Xiucai Ding; Hau-Tieng Wu (CoRR, 2021)

A Riemann-Hilbert approach to the perturbation theory for orthogonal polynomials: Applications to numerical linear algebra and random matrix theory. - Xiucai Ding; Thomas Trogdon (CoRR, 2021)

On the Spectral Property of Kernel-Based Sensor Fusion Algorithms of High Dimensional Data. - Xiucai Ding; Hau-Tieng Wu (IEEE Trans. Inf. Theory, 2021)

Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach. - Xiucai Ding; Rong Ma (CoRR, 2022)

Edge statistics of large dimensional deformed rectangular matrices. - Xiucai Ding; Fan Yang (J. Multivar. Anal., 2022)

Tracy-Widom Distribution for Heterogeneous Gram Matrices With Applications in Signal Detection. - Xiucai Ding; Fan Yang (IEEE Trans. Inf. Theory, 2022)

Impact of Signal-to-Noise Ratio and Bandwidth on Graph Laplacian Spectrum From High-Dimensional Noisy Point Cloud. - Xiucai Ding; Hau-Tieng Wu (IEEE Trans. Inf. Theory, 2023)

Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators. - Xiucai Ding; Rong Ma (CoRR, 2024)

Global and Local CLTs for Linear Spectral Statistics of General Sample Covariance Matrices When the Dimension Is Much Larger Than the Sample Size With Applications. - Xiucai Ding; Zhenggang Wang (IEEE Trans. Inf. Theory, 2025)