6 papers
System-Wide Termination in Distributed Betweenness Centrality Computation
Siamak Abdi, Lucia Cavallaro, Giuseppe Di Fatta
Computing betweenness centrality on large networks is inherently expensive, as it requires aggregating shortest-path dependencies across all pairs of vertices and becomes increasin…
Operator-Theoretic Generalization Bounds for Multitask Deep Learning
Mahdi Mohammadigohari, Thomas Borsani, Giuseppe Di Fatta
We develop operator-theoretic generalization bounds for deep multi-output function classes by representing network layers as Koopman composition operators on vector-valued reproduc…
Brownian Kernel Ladders
Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1
We introduce Brownian kernel ladders (BKLs), a recursive hierarchy of integral reproducing kernel Hilbert spaces built from linear functionals by repeatedly integrating Brownian pu…
High-quality data augmentation for code comment classification
Thomas Borsani, Andrea Rosani, Giuseppe Di Fatta
Code comments serve a crucial role in software development for documenting functionality, clarifying design choices, and assisting with issue tracking. They capture developers' ins…
On the Koopman-Based Generalization Bounds for Multi-Task Deep Learning
Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1
The paper establishes generalization bounds for multitask deep neural networks using operator-theoretic techniques. The authors propose a tighter bound than those derived from conv…
Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning
Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1
This paper presents novel generalization bounds for vector-valued neural networks and deep kernel methods, focusing on multi-task learning through an operator-theoretic framework.…