5 papers
A Filtered Mixture-of-Generators for Fully Synthetic Survival Training
Niccolò Maria Rizzi, Eugenio Lomurno, Alberto Archetti +1
Survival analysis models time-to-event data, but in clinical settings training data are costly and scarce: events accrue over years of follow-up, cohorts are small, and privacy reg…
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…
SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks
Marina Mastroleo, Alberto Archetti, Federico Mastroleo +1
Accurate prediction of time-to-event outcomes is critical for clinical decision-making, treatment planning, and resource allocation in modern healthcare. While classical survival m…
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis
Alberto Archetti, Eugenio Lomurno, Diego Piccinotti +1
Survival analysis is a statistical framework for modeling time-to-event data. It plays a pivotal role in medicine, reliability engineering, and social science research, where under…
Deep Variational Contrastive Learning for Joint Risk Stratification and Time-to-Event Estimation
Pinar Erbil, Alberto Archetti, Eugenio Lomurno +1
Survival analysis is essential for clinical decision-making, as it allows practitioners to estimate time-to-event outcomes, stratify patient risk profiles, and guide treatment plan…