458 citations · 502 across the 9 of their papers we have counts for
12 papers · 1 filter
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
Tengfei Ma, Trong Nghia Hoang, Jie Chen
Learning an effective global model on private and decentralized datasets has become an increasingly important challenge of machine learning when applied in practice. Existing distr…
Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction
Yinan Liu, Xinyu Dong, Weimin Lyu +4
Class imbalance problems widely exist in the medical field and heavily deteriorates performance of clinical predictive models. Most techniques to alleviate the problem rebalance cl…
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description
Ruixuan Yan, Tengfei Ma, Achille Fokoue +2
Most existing Time series classification (TSC) models lack interpretability and are difficult to inspect. Interpretable machine learning models can aid in discovering patterns in d…
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
Cao Xiao, Trong Nghia Hoang, Shenda Hong +2
There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e.g., intensi…
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Tengfei Ma, Jie Chen
Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby…
GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
Tengfei Ma, Junyuan Shang, Cao Xiao +1
Gaining more comprehensive knowledge about drug-drug interactions (DDIs) is one of the most important tasks in drug development and medical practice. Recently graph neural networks…