122 citations · 397 across the 29 of their papers we have counts for
30 papers
Enhancing XAI Narratives through Multi-Narrative Refinement and Knowledge Distillation
Flavio Giorgi, Matteo Silvestri, Cesare Campagnano +2
Explainable Artificial Intelligence has become a crucial area of research, aiming to demystify the decision-making processes of deep learning models. Among various explainability t…
When Large Language Models Know the Table: A Framework for Assessing Data Contamination in Tabular Datasets
Matteo Silvestri, Fabiano Veglianti, Flavio Giorgi +2
Large language models (LLMs) are increasingly exposed to data contamination, i.e., performance gains driven by prior exposure of test datasets rather than generalization. However,…
Evading Overlapping Community Detection via Proxy Node Injection
Dario Loi, Matteo Silvestri, Fabrizio Silvestri +1
Protecting privacy in social graphs requires preventing sensitive information, such as community affiliations, from being inferred by graph analysis, without substantially altering…
Demystifying Sequential Recommendations: Counterfactual Explanations via Genetic Algorithms
Domiziano Scarcelli, Filippo Betello, Giuseppe Perelli +2
Sequential Recommender Systems (SRSs) have demonstrated remarkable effectiveness in capturing users' evolving preferences. However, their inherent complexity as "black box" models…
Generalizability vs. Counterfactual Explainability Trade-Off
Fabiano Veglianti, Flavio Giorgi, Fabrizio Silvestri +1
In this work, we investigate the relationship between model generalization and counterfactual explainability in supervised learning. We introduce the notion of -valid…
One Search Fits All: Pareto-Optimal Eco-Friendly Model Selection
Filippo Betello, Antonio Purificato, Vittoria Vineis +2
The environmental impact of Artificial Intelligence (AI) is emerging as a significant global concern, particularly regarding model training. In this paper, we introduce GREEN (Guid…
FROG: Fair Removal on Graphs
Ziheng Chen, Jiali Cheng, Hadi Amiri +5
With growing emphasis on privacy regulations, machine unlearning has become increasingly critical in real-world applications such as social networks and recommender systems, many o…
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…
Countering Overfitting with Counterfactual Examples
Flavio Giorgi, Fabiano Veglianti, Fabrizio Silvestri +1
Overfitting is a well-known issue in machine learning that occurs when a model struggles to generalize its predictions to new, unseen data beyond the scope of its training set. Tra…
Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making
Vittoria Vineis, Giuseppe Perelli, Gabriele Tolomei
Conventional automated decision-support systems often prioritize predictive accuracy, overlooking the complexities of real-world settings where stakeholders' preferences may diverg…
The Right to Hide: Masking Community Affiliation via Minimal Graph Rewiring
Matteo Silvestri, Edoardo Gabrielli, Fabrizio Silvestri +1
Protecting privacy in social graphs may require obscuring nodes' membership in sensitive communities. However, doing so without significantly disrupting the underlying graph topolo…
Natural Language Counterfactual Explanations for Graphs Using Large Language Models
Flavio Giorgi, Cesare Campagnano, Fabrizio Silvestri +1
Explainable Artificial Intelligence (XAI) has emerged as a critical area of research to unravel the opaque inner logic of (deep) machine learning models. Among the various XAI tech…
Debiasing Machine Unlearning with Counterfactual Examples
Ziheng Chen, Jia Wang, Jun Zhuang +7
The right to be forgotten (RTBF) seeks to safeguard individuals from the enduring effects of their historical actions by implementing machine-learning techniques. These techniques…
: Gradient-based and Task-Agnostic machine Unlearning
Daniel Trippa, Cesare Campagnano, Maria Sofia Bucarelli +2
Machine Unlearning, the process of selectively eliminating the influence of certain data examples used during a model's training, has gained significant attention as a means for pr…
Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models
Gabriele Tolomei, Cesare Campagnano, Fabrizio Silvestri +1
In this paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system. Within this innovative framewo…
Evading Community Detection via Counterfactual Neighborhood Search
Andrea Bernini, Fabrizio Silvestri, Gabriele Tolomei
Community detection techniques are useful for social media platforms to discover tightly connected groups of users who share common interests. However, this functionality often com…
A Survey on Decentralized Federated Learning
Edoardo Gabrielli, Anthony Di Pietro, Dario Fenoglio +2
Federated learning (FL) enables collaborative training without pooling raw data, but standard FL relies on a central coordinator, which introduces a single point of failure and con…
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
Ziheng Chen, Fabrizio Silvestri, Jia Wang +2
Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial int…
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
Edoardo Gabrielli, Dimitri Belli, Zoe Matrullo +2
Current defense mechanisms against model poisoning attacks in federated learning (FL) systems have proven effective up to a certain threshold of malicious clients. In this work, we…
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…
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Ziheng Chen, Fabrizio Silvestri, Jia Wang +4
Recently, graph neural networks (GNNs) have been widely used to develop successful recommender systems. Although powerful, it is very difficult for a GNN-based recommender system t…
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…
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
Ziheng Chen, Fabrizio Silvestri, Jia Wang +3
Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods eithe…