113 citations · 150 across the 12 of their papers we have counts for
12 papers
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
Alessandro Bregoli, Karin Rathsman, Marco Scutari +2
Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is importa…
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas +4
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models…
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas +4
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are…
A Survey on Causal Discovery: Theory and Practice
Alessio Zanga, Elif Ozkirimli, Fabio Stella
Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a c…
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains
Francesco Stranieri, Fabio Stella
In this study, we analyze and compare the performance of state-of-the-art deep reinforcement learning algorithms for solving the supply chain inventory management problem. This com…
Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches
Francesco Craighero, Fabrizio Angaroni, Fabio Stella +3
A key challenge in computer vision and deep learning is the definition of robust strategies for the detection of adversarial examples. Here, we propose the adoption of ensemble app…
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases
Gabriele Sottocornola, Fabio Stella, Markus Zanker
Post-harvest diseases of apple are one of the major issues in the economical sector of apple production, causing severe economical losses to producers. Thus, we developed DSSApple,…
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Andrea Ruggieri, Francesco Stranieri, Fabio Stella +1
Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their grap…
A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks
Alessandro Bregoli, Marco Scutari, Fabio Stella
Dynamic Bayesian networks have been well explored in the literature as discrete-time models: however, their continuous-time extensions have seen comparatively little attention. In…
Investigating the Compositional Structure Of Deep Neural Networks
Francesco Craighero, Fabrizio Angaroni, Alex Graudenzi +2
The current understanding of deep neural networks can only partially explain how input structure, network parameters and optimization algorithms jointly contribute to achieve the s…
CTBNCToolkit: Continuous Time Bayesian Network Classifier Toolkit
Daniele Codecasa, Fabio Stella
Continuous time Bayesian network classifiers are designed for temporal classification of multivariate streaming data when time duration of events matters and the class does not cha…
Self Organizing Maps to efficiently cluster and functionally interpret protein conformational ensembles
Domenico Fraccalvieri, Laura Bonati, Fabio Stella
An approach that combines Self-Organizing maps, hierarchical clustering and network components is presented, aimed at comparing protein conformational ensembles obtained from multi…