no code implementations • 5 Jan 2021 • Zhenhua Lin, Dehan Kong, Linbo Wang
Understanding causal relationships is one of the most important goals of modern science.
Causal Inference
Methodology
no code implementations • 10 Dec 2020 • Ying Zhou, Dehan Kong, Linbo Wang
In contrast to existing proposals in the literature, the roles of multiple outcomes in our key identification assumption are symmetric, hence the name parallel outcomes.
Causal Inference
Methodology
1 code implementation • 21 Sep 2020 • Ming Y. Lu, Dehan Kong, Jana Lipkova, Richard J. Chen, Rajendra Singh, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood
In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy.
no code implementations • 11 Apr 2020 • Yichi Zhang, Weining Shen, Dehan Kong
Covariance estimation for matrix-valued data has received an increasing interest in applications.
no code implementations • 31 Jul 2019 • Dehan Kong, Shu Yang, Linbo Wang
Unobserved confounding presents a major threat to causal inference in observational studies.
Methodology
no code implementations • 24 Sep 2018 • Wei Hu, Weining Shen, Hua Zhou, Dehan Kong
We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies.