59 citations · 106 across the 13 of their papers we have counts for
15 papers
FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning
Brianna Mueller, W. Nick Street
Statistical heterogeneity in Federated Learning (FL) often leads to negative transfer, where a single global model fails to serve diverse client distributions. Personalized federat…
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning
Brianna Mueller, W. Nick Street, Stephen Baek +3
Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. However, existing FL parad…
An Explainable Deep Reinforcement Learning Model for Warfarin Maintenance Dosing Using Policy Distillation and Action Forging
Sadjad Anzabi Zadeh, W. Nick Street, Barrett W. Thomas
Deep Reinforcement Learning is an effective tool for drug dosing for chronic condition management. However, the final protocol is generally a black box without any justification fo…
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data
Bang An, Amin Vahedian, Xun Zhou +2
Traffic accident forecasting is a significant problem for transportation management and public safety. However, this problem is challenging due to the spatial heterogeneity of the…
Optimizing Warfarin Dosing using Deep Reinforcement Learning
Sadjad Anzabi Zadeh, W. Nick Street, Barrett W. Thomas
Warfarin is a widely used anticoagulant, and has a narrow therapeutic range. Dosing of warfarin should be individualized, since slight overdosing or underdosing can have catastroph…
Gaussian Process Regression and Classification using International Classification of Disease Codes as Covariates
Sanvesh Srivastava, Zongyi Xu, Yunyi Li +2
International Classification of Disease (ICD) codes are widely used for encoding diagnoses in electronic health records (EHR). Automated methods have been developed over the years…
A predictive model for kidney transplant graft survival using machine learning
Eric S. Pahl, W. Nick Street, Hans J. Johnson +1
Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney don…
Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification
Michael T. Lash, W. Nick Street
Cardiovascular disease (CVD) is a serious illness affecting millions world-wide and is the leading cause of death in the US. Recent years, however, have seen tremendous growth in t…
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
Amin Vahedian, Xun Zhou, Ling Tong +2
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigatin…
Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer Survival Curves in Iowa
Michael T. Lash, Min Zhang, Xun Zhou +2
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore differe…
Prophit: Causal inverse classification for multiple continuously valued treatment policies
Michael T. Lash, Qihang Lin, W. Nick Street
Inverse classification uses an induced classifier as a queryable oracle to guide test instances towards a preferred posterior class label. The result produced from the process is a…
Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa
Michael T. Lash, Yuqi Sun, Xun Zhou +2
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the us…
Realistic risk-mitigating recommendations via inverse classification
Michael T. Lash, W. Nick Street
Inverse classification, the process of making meaningful perturbations to a test point such that it is more likely to have a desired classification, has previously been addressed u…
Generalized Inverse Classification
Michael T. Lash, Qihang Lin, W. Nick Street +2
Inverse classification is the process of perturbing an instance in a meaningful way such that it is more likely to conform to a specific class. Historical methods that address such…
A budget-constrained inverse classification framework for smooth classifiers
Michael T. Lash, Qihang Lin, W. Nick Street +1
Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcom…