7 citations · 16 across the 8 of their papers we have counts for
8 papers
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…
Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation
Xiaohan Li, Yuqing Liu, Zheng Liu +1
Session-based Recommendation (SBR) is to predict users' next interested items based on their previous browsing sessions. Existing methods model sessions as graphs or sequences to e…
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Xiaohan Li, Zheng Liu, Luyi Ma +4
Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequ…
Collaborative Bi-Aggregation for Directed Graph Embedding
Linsong Liu, Kejia Chen, Zheng Liu
Directed graphs model asymmetric relationships between nodes and research on directed graph embedding is of great significance in downstream graph analysis and inference. Learning…
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…
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
Xiaohan Li, Zhiwei Liu, Stephen Guo +4
Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. H…
Heterogeneous Similarity Graph Neural Network on Electronic Health Records
Zheng Liu, Xiaohan Li, Hao Peng +2
Mining Electronic Health Records (EHRs) becomes a promising topic because of the rich information they contain. By learning from EHRs, machine learning models can be built to help…
Dynamic Graph Collaborative Filtering
Xiaohan Li, Mengqi Zhang, Shu Wu +3
Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and i…