2 papers
cs.LG2026
QuAIL: Quality-Aware Inertial Learning for Robust Training under Data Corruption
Mattia Sabella, Alberto Archetti, Pietro Pinoli +2
Tabular machine learning systems are frequently trained on data affected by non-uniform corruption, including noisy measurements, missing entries, and feature-specific biases. In p…
cs.DB2025
I-ETL: an interoperability-aware health (meta) data pipeline to enable federated analyses
Nelly Barret, Anna Bernasconi, Boris Bikbov +1
Clinicians are interested in better understanding complex diseases, such as cancer or rare diseases, so they need to produce and exchange data to mutualize sources and join forces.…