Federated Adversarial Debiasing for Fair and Transferable Representations

1 code implementation • the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining 2021 • Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Zhangyang Wang, Hiroko Dodge, Jiayu Zhou

While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework.

Federated Learning Unsupervised Domain Adaptation

Topic-Based Measures of Conversation for Detecting Mild CognitiveImpairment

no code implementations • WS 2020 • Meysam Asgari, Liu Chen, Hiroko Dodge

Conversation is a complex cognitive task that engages multiple aspects of cognitive functions to remember the discussed topics, monitor the semantic and linguistic elements, and recognize others{'} emotions.

MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records

1 code implementation • 8 May 2019 • Xi Sheryl Zhang, Fengyi Tang, Hiroko Dodge, Jiayu Zhou, Fei Wang

In this paper, we propose MetaPred, a meta-learning for clinical risk prediction from longitudinal patient EHRs.

Meta-Learning