13 papers
Structured Sheaf Learning of Consistent Connection Graphs
Leonardo Di Nino, Gabriele D'Acunto, Sergio Barbarossa +1
Connection graphs (CGs) extend classical graphs by associating vector-valued signals to nodes and orthogonal transport maps across edges, making them a natural model for synchroniz…
Sheaf-Based Federated Representation Learning
Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino +4
Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing modalities, model architectures,…
Sheaf-theoretic Signal Processing on Graphs: Spectral Theory, Filtering, and Sampling
Gabriele D'Acunto, Leonardo Di Nino, Paolo Di Lorenzo +1
Modern sensing, communication, and learning systems generate heterogeneous network signals, with local data differing in dimension, modality, and geometric structure. Processing su…
A Sheaf-Theoretic Framework for Distributed Multi-Site Channel Charting
Enrico Grimaldi, Leonardo Di Nino, Mario Edoardo Pandolfo +3
Channel charting (CC) enables data-driven user localization in wireless networks by embedding channel state information (CSI) into low-dimensional representations. In multi-cell sc…
Networks of Causal Abstractions: A Sheaf-theoretic Framework
Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
A core challenge in causal artificial intelligence is the principled coordination of multiple, imperfect, and subjective causal perspectives arising from distributed agents with li…
Learning Consistent Causal Abstraction Networks
Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
Causal artificial intelligence aims to enhance explainability, trustworthiness, and robustness in AI by leveraging structural causal models (SCMs). In this pursuit, recent advances…