6 papers
Democratising Clinical AI through Dataset Condensation for Classical Clinical Models
Anshul Thakur, Soheila Molaei, Pafue Christy Nganjimi +5
Dataset condensation (DC) learns a compact synthetic dataset that enables models to match the performance of full-data training, prioritising utility over distributional fidelity.…
RiskAgent: Synergizing Language Models with Validated Tools for Evidence-Based Risk Prediction
Fenglin Liu, Jinge Wu, Hongjian Zhou +9
Large Language Models (LLMs) achieve competitive results compared to human experts in medical examinations. However, it remains a challenge to apply LLMs to complex clinical decisi…
Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Edward Phillips, Sean Wu, Soheila Molaei +3
Large language models demonstrate impressive results across diverse tasks but are still known to hallucinate, generating linguistically plausible but incorrect answers to questions…
Aggregation on Learnable Manifolds for Asynchronous Federated Optimization
Archie Licudi, Anshul Thakur, Soheila Molaei +2
Asynchronous federated learning (FL) with heterogeneous clients faces two key issues: curvature-induced loss barriers encountered by standard linear parameter interpolation techniq…
DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation
Munib Mesinovic, Soheila Molaei, Peter Watkinson +1
Learning from longitudinal electronic health records is limited if it does not capture the temporal trajectories of the patient's state in a clinical setting. Graph models allow us…
Efficient Task Grouping Through Samplewise Optimisation Landscape Analysis
Anshul Thakur, Yichen Huang, Soheila Molaei +2
Shared training approaches, such as multi-task learning (MTL) and gradient-based meta-learning, are widely used in various machine learning applications, but they often suffer from…