5 citations · 10 across the 17 of their papers we have counts for
8 papers · 1 filter
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-…