6 papers
Artificial Effort
Federico Belotti, Stefano Coniglio, Antonio Cosma +1
Real-effort tasks, in which participants perform cognitively costly activities whose outcomes depend on actual performance, are widely used in experimental economics. Their validit…
Directional Sheaf Hypergraph Networks: Unifying Learning on Directed and Undirected Hypergraphs
Emanuele Mule, Stefano Fiorini, Antonio Purificato +3
Hypergraphs provide a natural way to represent higher-order interactions among multiple entities. While undirected hypergraphs have been extensively studied, the case of directed h…
Adversarial training with restricted data manipulation
David Benfield, Stefano Coniglio, Phan Tu Vuong +1
Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as spam email filtering, malware…
Sheaves Reloaded: A Directional Awakening
Stefano Fiorini, Hakan Aktas, Iulia Duta +4
Sheaf Neural Networks (SNNs) represent a powerful generalization of Graph Neural Networks (GNNs) that significantly improve our ability to model complex relational data. While dire…
Classification under strategic adversary manipulation using pessimistic bilevel optimisation
David Benfield, Stefano Coniglio, Martin Kunc +2
Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as spam email filtering, malware…
DLGNet: Hyperedge Classification through Directed Line Graphs for Chemical Reactions
Stefano Fiorini, Giulia M. Bovolenta, Stefano Coniglio +4
Graphs and hypergraphs provide powerful abstractions for modeling interactions among a set of entities of interest and have been attracting a growing interest in the literature tha…