10.1k citations · 21.9k across the 280 of their papers we have counts for
69 papers · 1 filter
A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder
Zheng Liu, Xiaohan Li, Philip Yu
The fairness issue of clinical data modeling, especially on Electronic Health Records (EHRs), is of utmost importance due to EHR's complex latent structure and potential selection…
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Xuan Lin, Lichang Dai, Yafang Zhou +9
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially…
Mitigating Health Disparities in EHR via Deconfounder
Zheng Liu, Xiaohan Li, Philip Yu
Health disparities, or inequalities between different patient demographics, are becoming crucial in medical decision-making, especially in Electronic Health Record (EHR) predictive…
Improving Contrastive Learning with Model Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity a…
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
Yang Shu, Zhangjie Cao, Jinghan Gao +3
Few-shot learning aims to fast adapt a deep model from a few examples. While pre-training and meta-training can create deep models powerful for few-shot generalization, we find tha…
Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities
Mehrnaz Najafi, Lifang He, Philip S. Yu
With recent advances in data collection from multiple sources, multi-view data has received significant attention. In multi-view data, each view represents a different perspective…