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20242026
most citedRiskAgent: Synergizing Language Models with Validated Tools for Evidence-Based Risk Prediction

2 citations · 2 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons

Wei Tang, Jinpei Han, Kangning Cui +10

Electrocardiogram (ECG) foundation models pretrained on typical diagnostic 10-second ECG segments, have demonstrated strong transferability across a range of clinical applications.…

cs.LG2026

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.…

cs.LG20262 cited

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…

cs.LG2025

Improving Clinical Dataset Condensation with Mode Connectivity-based Trajectory Surrogates

Pafue Christy Nganjimi, Andrew Soltan, Danielle Belgrave +3

Dataset condensation (DC) enables the creation of compact, privacy-preserving synthetic datasets that can match the utility of real patient records, supporting democratised access…

cs.LG2025

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…

cs.LG2024

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…