56 citations · 182 across the 17 of their papers we have counts for
17 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…
Bayesian Kernelised Test of (In)dependence with Mixed-type Variables
Alessio Benavoli, Cassio de Campos
A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound). We propose a Bayesian kernelised correlation test of (in)dependence using a…
Towards Robust Classification with Deep Generative Forests
Alvaro H. C. Correia, Robert Peharz, Cassio de Campos
Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets. Nonet…
Joints in Random Forests
Alvaro H. C. Correia, Robert Peharz, Cassio de Campos
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist.…
On Pruning for Score-Based Bayesian Network Structure Learning
Alvaro H. C. Correia, James Cussens, Cassio de Campos
Many algorithms for score-based Bayesian network structure learning (BNSL), in particular exact ones, take as input a collection of potentially optimal parent sets for each variabl…
Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos +1
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian greatly reduces the comp…