Causal Inference on Distribution Functions

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

The Promises of Parallel Outcomes

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

Federated Learning for Computational Pathology on Gigapixel Whole Slide Images

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.

Federated Learning Multiple Instance Learning +4

Covariance Estimation for Matrix-valued Data

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.

Multi-cause causal inference with unmeasured confounding and binary outcome

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

Matrix Linear Discriminant Analysis

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.

General Classification regression