Anshul THAKUR

Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks. In International Conference on Artificial Intelligence and Statistics (pp. 1342-1350). PMLR.

Incremental Trainable Parameter Selection in Deep Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. Pp. 1-14. DOI: 10.1109/TNNLS.2022.3210297

Information Transfer Across Clinical Tasks via Adaptive Parameter Optimisation. In The 28th International Conference on Artificial Intelligence and Statistics.https://proceedings.mlr.press/v258/thakur25b.html

Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare. npj Digital Medicine, 7(1), 283. https://doi.org/10.1038/s41746-024-01272-9

Learning Across the Divide: Personalised Federated Learning for Robust Clinical Modelling under Data-View Heterogeneity. IEEE Journal of Biomedical and Health Informatics.https://doi.org/10.1109/JBHI.2025.3611069

Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation. In International Conference on Artificial Intelligence and Statistics (pp. 163-171). PMLR.https://proceedings.mlr.press/v258/thakur25a.html

Prediction of COVID-19 hospitalisation, ICU admission or death following ChAdOx1 vaccination using artificial intelligence: A clinical predictive model from the English RAVEN study. [Plos one, 21(2), e0336449.]

Self-aware sgd: Reliable incremental adaptation framework for clinical ai models. IEEE Journal of Biomedical and Health Informatics, 27(3), 1624-1634. DOI: 10.1109/JBHI.2023.3237592