24 citations · 29 across the 3 of their papers we have counts for
4 papers
Shapley variable importance clouds for interpretable machine learning
Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty +4
Interpretable machine learning has been focusing on explaining final models that optimize performance. The current state-of-the-art is the Shapley additive explanations (SHAP) that…
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Zidi Xiu, Junya Chen, Ricardo Henao +3
Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of pr…
Variational Disentanglement for Rare Event Modeling
Zidi Xiu, Chenyang Tao, Michael Gao +3
Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical d…
Adversarial Time-to-Event Modeling
Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li +4
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support…