5 citations · 10 across the 17 of their papers we have counts for
19 papers
Inverse Cross-spectral Neural Networks for Multivariate Time Series
Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto +2
CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators directly from second-order statisti…
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…
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…
Colored Markov Random Fields for Probabilistic Topological Modeling
Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto +3
Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint dis…
Learning Network Sheaves for AI-native Semantic Communication
Enrico Grimaldi, Mario Edoardo Pandolfo, Gabriele D'Acunto +2
Recent advances in AI call for a paradigm shift from bit-centric communication to goal- and semantics-oriented architectures, paving the way for AI-native 6G networks. In this cont…
Simplicial Gaussian Models: Representation and Inference
Lorenzo Marinucci, Gabriele D'Acunto, Paolo Di Lorenzo +1
Probabilistic graphical models (PGMs) are powerful tools for representing statistical dependencies through graphs in high-dimensional systems. However, they are limited to pairwise…
Learning the Structure of Connection Graphs
Leonardo Di Nino, Gabriele D'Acunto, Sergio Barbarossa +1
Connection graphs (CGs) extend traditional graph models by coupling network topology with orthogonal transformations, enabling the representation of global geometric consistency. T…
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…
Risk-aware Trading Portfolio Optimization
Marco Bianchetti, Gabriele D'Acunto, Gianmarco De Francisci Morales +3
We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO), form…
The Relativity of Causal Knowledge
Gabriele D'Acunto, Claudio Battiloro
Recent advances in artificial intelligence reveal the limits of purely predictive systems and call for a shift toward causal and collaborative reasoning. Drawing inspiration from t…
Causal Abstraction Learning based on the Semantic Embedding Principle
Gabriele D'Acunto, Fabio Massimo Zennaro, Yorgos Felekis +1
Structural causal models (SCMs) allow us to investigate complex systems at multiple levels of resolution. The causal abstraction (CA) framework formalizes the mapping between high-…
Learning Multi-Frequency Partial Correlation Graphs
Gabriele D'Acunto, Paolo Di Lorenzo, Francesco Bonchi +2
Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlat…
Extracting the Multiscale Causal Backbone of Brain Dynamics
Gabriele D'Acunto, Francesco Bonchi, Gianmarco De Francisci Morales +1
The bulk of the research effort on brain connectivity revolves around statistical associations among brain regions, which do not directly relate to the causal mechanisms governing…
Learning Multiscale Non-stationary Causal Structures
Gabriele D'Acunto, Gianmarco De Francisci Morales, Paolo Bajardi +1
This paper addresses a gap in the current state of the art by providing a solution for modeling causal relationships that evolve over time and occur at different time scales. Speci…
Multiscale Causal Structure Learning
Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system. This paper exposes a novel method, named Multiscale-Causa…
The Evolving Causal Structure of Equity Risk Factors
Gabriele D'Acunto, Paolo Bajardi, Francesco Bonchi +1
In recent years, multi-factor strategies have gained increasing popularity in the financial industry, as they allow investors to have a better understanding of the risk drivers und…