27 citations · 32 across the 5 of their papers we have counts for
5 papers
On the Properties of Feature Attribution for Supervised Contrastive Learning
Leonardo Arrighi, Julia Eva Belloni, Aurélie Gallet +3
Most Neural Networks (NNs) for classification are trained using Cross-Entropy as a loss function. This approach requires the model to have an explicit classification layer. However…
Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
Matheus Camilo da Silva, Leonardo Arrighi, Ana Carolina Lorena +1
AutoClustering methods aim to automate unsupervised learning tasks, including algorithm selection (AS), hyperparameter optimization (HPO), and pipeline synthesis (PS), by often lev…
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest
Matteo Ceschin, Leonardo Arrighi, Luca Longo +1
The need to explain predictive models is well-established in modern machine learning. However, beyond model interpretability, understanding pre-processing methods is equally essent…
Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review
Leonardo Arrighi, Ingrid Alves de Moraes, Marco Zullich +3
Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numerous disciplines beyond computer s…
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
Leonardo Arrighi, Luca Pennella, Gabriel Marques Tavares +1
Understanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop…