4 papers
Towards Privacy-Aware Bayesian Networks: A Credal Approach
Niccolò Rocchi, Fabio Stella, Cassio de Campos
Bayesian networks (BN) are probabilistic graphical models that enable efficient knowledge representation and inference. These have proven effective across diverse domains, includin…
Towards conservative inference in credal networks using belief functions: the case of credal chains
Marco Sangalli, Thomas Krak, Cassio de Campos
This paper explores belief inference in credal networks using Dempster-Shafer theory. By building on previous work, we propose a novel framework for propagating uncertainty through…
Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits
Gennaro Gala, Cassio de Campos, Antonio Vergari +1
Probabilistic integral circuits (PICs) have been recently introduced as probabilistic models enjoying the key ingredient behind expressive generative models: continuous latent vari…
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?
Lorenzo Loconte, Antonio Mari, Gennaro Gala +5
This paper establishes a rigorous connection between circuit representations and tensor factorizations, two seemingly distinct yet fundamentally related areas. By connecting these…