6 citations · 8 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing
Eugenio Lomurno, Matteo Matteucci
Federated learning has emerged as a paradigm for collaborative learning, enabling the development of robust models without the need to centralise sensitive data. However, conventio…
Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning
Riccardo Ughi, Eugenio Lomurno, Matteo Matteucci
The Transformer is a highly successful deep learning model that has revolutionised the world of artificial neural networks, first in natural language processing and later in comput…
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique
Andrea Falanti, Eugenio Lomurno, Stefano Samele +2
Automating the research for the best neural network model is a task that has gained more and more relevance in the last few years. In this context, Neural Architecture Search (NAS)…