6 papers · 1 filter
FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
Xenia Heilmann, Luca Corbucci, Mattia Cerrato +1
Federated Learning (FL) enables collaborative training while preserving privacy, yet it introduces a critical challenge: the "illusion of fairness''. A global model, usually evalua…
Bounded-Abstention Multi-horizon Time-series Forecasting
Luca Stradiotti, Laurens Devos, Anna Monreale +2
Multi-horizon time-series forecasting involves simultaneously making predictions for a consecutive sequence of subsequent time steps. This task arises in many application domains,…
Causal Synthetic Data Generation in Recruitment
Andrea Iommi, Antonio Mastropietro, Riccardo Guidotti +2
The importance of Synthetic Data Generation (SDG) has increased significantly in domains where data quality is poor or access is limited due to privacy and regulatory constraints.…
An Interpretable Data-Driven Unsupervised Approach for the Prevention of Forgotten Items
Luca Corbucci, Javier Alejandro Borges Legrottaglie, Francesco Spinnato +2
Accurately identifying items forgotten during a supermarket visit and providing clear, interpretable explanations for recommending them remains an underexplored problem within the…
Explainable AI in Time-Sensitive Scenarios: Prefetched Offline Explanation Model
Fabio Michele Russo, Carlo Metta, Anna Monreale +2
As predictive machine learning models become increasingly adopted and advanced, their role has evolved from merely predicting outcomes to actively shaping them. This evolution has…
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
Luca Corbucci, Mikko A Heikkila, David Solans Noguero +2
Training and deploying Machine Learning models that simultaneously adhere to principles of fairness and privacy while ensuring good utility poses a significant challenge. The inter…