112 citations · 186 across the 11 of their papers we have counts for
11 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.…
Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Edward Phillips, Sean Wu, Soheila Molaei +3
Large language models are known to hallucinate, generating linguistically plausible but incorrect answers to questions. Uncertainty quantification has been proposed as a strategy t…
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…
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…
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…
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…
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
Xin Zheng, Miao Zhang, Chunyang Chen +3
Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant performance uncert…
A Brief Review of Hypernetworks in Deep Learning
Vinod Kumar Chauhan, Jiandong Zhou, Ping Lu +2
Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learn…
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
Vinod Kumar Chauhan, Jiandong Zhou, Ghadeer Ghosheh +2
Estimation of individualized treatment effects (ITE) from observational studies is a fundamental problem in causal inference and holds significant importance across domains, includ…
Adversarial De-confounding in Individualised Treatment Effects Estimation
Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania +3
Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the…
Deep Learning Approach on Information Diffusion in Heterogeneous Networks
Soheila Molaei, Hadi Zare, Hadi Veisi
There are many real-world knowledge based networked systems with multi-type interacting entities that can be regarded as heterogeneous networks including human connections and biol…