4 citations · 6 across the 7 of their papers we have counts for
7 papers
A Novel Multi-view Mixture Model Framework for Longitudinal Clustering with Application to ANCA-Associated Vasculitis
Shen Jia, David Selby, Mark A Little +1
Effectively modeling irregularly sampled longitudinal data is essential for understanding disease progression and improving risk prediction. We propose a two-view mixture model tha…
Neural Spatiotemporal Point Processes: Trends and Challenges
Sumantrak Mukherjee, Mouad Elhamdi, George Mohler +4
Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and he…
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning
Yousef Koka, David Selby, Gerrit Großmann +2
Data preprocessing is often paid little attention in machine learning, despite its potentially significant impact on model performance. While automated machine learning pipelines a…
Rethinking Cancer Gene Identification through Graph Anomaly Analysis
Yilong Zang, Lingfei Ren, Yue Li +7
Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insuff…
PageRank and the Bradley-Terry model
David Antony Selby
PageRank and the Bradley-Terry model are competing approaches to ranking entities such as teams in sports tournaments or journals in citation networks. The Bradley-Terry model is a…
Had enough of experts? Quantitative knowledge retrieval from large language models
David Selby, Kai Spriestersbach, Yuichiro Iwashita +6
Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information…
X Hacking: The Threat of Misguided AutoML
Rahul Sharma, Sergey Redyuk, Sumantrak Mukherjee +4
Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to…