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A Theoretical Framework for Understanding and Building "Any-task inference" Capable Foundation Models
Matthew McDermott, Florent Pollet, Payal Chandak +4
In this work, we provide a formal framework and mathematical definition for the subset of foundation models focused on enabling the property of "any-task inference" (ATI) (similar…
Rethinking How We Evaluate Methodological Progress in Health AI
Florent Pollet, Matthew McDermott
Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work better, and under which condi…
FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records
Vincent Jeanselme, Zilin Jing, Aparajita Kashyap +9
Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (i…
PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning
Ahmed Elhussein, Florent Pollet, Gamze Gürsoy
Federated learning (FL) with non-IID data often degrades client performance below local training baselines. Partial FL addresses this by federating only early layers that learn tra…