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.
1 code implementation • 29 May 2023 • Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji
In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.
1 code implementation • 23 Nov 2023 • Bingkang Shi, Xiaodan Zhang, Dehan Kong, Yulei Wu, Zongzhen Liu, Honglei Lyu, Longtao Huang
The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application.
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.
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 • 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 • 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
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 • 26 May 2023 • Ruixiang Tang, Dehan Kong, Longtao Huang, Hui Xue
Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts).