An innovative approach for analysing rank deficient covariance matrices. - Gabriel H. Tucci; Ke Wang 003 (ISIT, 2012)

Sparse random graphs: Eigenvalues and eigenvectors. - Linh V. Tran; Van H. Vu; Ke Wang 003 (Random Struct. Algorithms, 2013)

Random weighted projections, random quadratic forms and random eigenvectors. - Van H. Vu; Ke Wang 003 (Random Struct. Algorithms, 2015)

Eigenvectors of random matrices: A survey. - Sean O'Rourke; Van Vu; Ke Wang 003 (J. Comb. Theory A, 2016)

Random perturbation and matrix sparsification and completion. - Sean O'Rourke; Van Vu; Ke Wang 003 (CoRR, 2018)

Enhanced Expressive Power and Fast Training of Neural Networks by Random Projections. - Jian-Feng Cai 001; Dong Li; Jiaze Sun; Ke Wang 003 (CoRR, 2018)

New Methods for Handling Singular Sample Covariance Matrices. - Gabriel H. Tucci; Ke Wang 003 (IEEE Trans. Inf. Theory, 2019)

Optimal Subspace Perturbation Bounds under Gaussian Noise. - Sean O'Rourke; Van Vu; Ke Wang 003 (ISIT, 2023)

Limiting empirical spectral distribution for the non-backtracking matrix of an Erdős-Rényi random graph. - Ke Wang 003; Philip Matchett Wood (Comb. Probab. Comput., 2023)

Matrices With Gaussian Noise: Optimal Estimates for Singular Subspace Perturbation. - Sean O'Rourke; Van Vu; Ke Wang 003 (IEEE Trans. Inf. Theory, 2024)