16 citations · 43 across the 13 of their papers we have counts for
13 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…
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…
Latent Neural Cellular Automata for Resource-Efficient Image Restoration
Andrea Menta, Alberto Archetti, Matteo Matteucci
Neural cellular automata represent an evolution of the traditional cellular automata model, enhanced by the integration of a deep learning-based transition function. This shift fro…
Harnessing the Computing Continuum across Personalized Healthcare, Maintenance and Inspection, and Farming 4.0
Fatemeh Baghdadi, Davide Cirillo, Daniele Lezzi +6
The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This…
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics
Alberto Archetti, Francesca Ieva, Matteo Matteucci
Survival analysis is a fundamental tool in medicine, modeling the time until an event of interest occurs in a population. However, in real-world applications, survival data are oft…
Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks
Eugenio Lomurno, Alberto Archetti, Francesca Ausonio +1
The remarkable proliferation of deep learning across various industries has underscored the importance of data privacy and security in AI pipelines. As the evolution of sophisticat…
Heterogeneous Datasets for Federated Survival Analysis Simulation
Alberto Archetti, Eugenio Lomurno, Francesco Lattari +2
Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering,…
Federated Survival Forests
Alberto Archetti, Matteo Matteucci
Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread app…
SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning
Eugenio Lomurno, Alberto Archetti, Lorenzo Cazzella +3
Privacy regulation laws, such as GDPR, impose transparency and security as design pillars for data processing algorithms. In this context, federated learning is one of the most inf…
Neural Weighted A*: Learning Graph Costs and Heuristics with Differentiable Anytime A*
Alberto Archetti, Marco Cannici, Matteo Matteucci
Recently, the trend of incorporating differentiable algorithms into deep learning architectures arose in machine learning research, as the fusion of neural layers and algorithmic l…