122 citations · 123 across the 5 of their papers we have counts for
6 papers
COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations
Flavio Giorgi, Fabrizio Silvestri, Gabriele Tolomei
Counterfactual explanations have emerged as a powerful tool to unveil the opaque decision-making processes of graph neural networks (GNNs). However, existing techniques primarily f…
MUSTACHE: Multi-Step-Ahead Predictions for Cache Eviction
Gabriele Tolomei, Lorenzo Takanen, Fabio Pinelli
In this work, we propose MUSTACHE, a new page cache replacement algorithm whose logic is learned from observed memory access requests rather than fixed like existing policies. We f…
Sparse Vicious Attacks on Graph Neural Networks
Giovanni Trappolini, Valentino Maiorca, Silvio Severino +3
Graph Neural Networks (GNNs) have proven to be successful in several predictive modeling tasks for graph-structured data. Amongst those tasks, link prediction is one of the fundame…
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks
Federico Siciliano, Maria Sofia Bucarelli, Gabriele Tolomei +1
In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts neuron structure. This new framework aims to explore additional desirable properties of artificial neuron…
Treant: Training Evasion-Aware Decision Trees
Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei +2
Despite its success and popularity, machine learning is now recognized as vulnerable to evasion attacks, i.e., carefully crafted perturbations of test inputs designed to force pred…
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines +1
Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretabil…