2 citations · 3 across the 3 of their papers we have counts for
6 papers
Fair Bayesian Data Selection via Generalized Discrepancy Measures
Yixuan Zhang, Jiabin Luo, Zhenggang Wang +2
Fairness concerns are increasingly critical as machine learning models are deployed in high-stakes applications. While existing fairness-aware methods typically intervene at the mo…
Counting Cycles with AI: Counting Cycles with AI: Computationally Efficient Equivalent Forms with Applications
Jiashun Jin, Zheng Tracy Ke, Bingcheng Sui +1
Cycle count statistics are fundamental tools in statistics and engineering, with applications in motif counting, channel coding, and statistical inference of network and matrix dat…
Alignment and matching tests for high-dimensional tensor signals via tensor contraction
Ruihan Liu, Zhenggang Wang, Jianfeng Yao
We consider two hypothesis testing problems for low-rank and high-dimensional tensor signals, namely the tensor signal alignment and tensor signal matching problems. These problems…
Two sample test for covariance matrices in ultra-high dimension
Xiucai Ding, Yichen Hu, Zhenggang Wang
In this paper, we propose a new test for testing the equality of two population covariance matrices in the ultra-high dimensional setting that the dimension is much larger than the…
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
In this paper, under the assumption that the dimension is much larger than the sample size, i.e., we consider the (unnormalized) sample covariance matrices $Q…
Central limit theorem for linear spectral statistics of block-Wigner-type matrices
Zhenggang Wang, Jianfeng Yao
Motivated by the stochastic block model, we investigate a class of Wigner-type matrices with certain block structures, and establish a CLT for the corresponding linear spectral sta…