5 citations · 6 across the 19 of their papers we have counts for
11 papers · 1 filter
PISA: Prioritized Invariant Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
Ali Ghasemi, Farooq Ahmad Wani, Maria Sofia Bucarelli +1
Invariant learning on graphs aims to build predictors that rely on causal substructures rather than on environment-specific shortcuts. Current methods extract either a single invar…
Subtract the Corruption: Training-Data-Free Corrective Machine Unlearning using Task Arithmetic
Mostafa Mozafari, Farooq Ahmad Wani, Maria Sofia Bucarelli +1
Corrupted training data are ubiquitous. Corrective Machine Unlearning (CMU) seeks to remove the influence of such corruption post-training. Prior CMU typically assumes access to id…
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…
On Task Vectors and Gradients
Luca Zhou, Daniele Solombrino, Donato Crisostomi +4
Task arithmetic has emerged as a simple yet powerful technique for model merging, enabling the combination of multiple finetuned models into one. Despite its empirical success, a c…
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…
MASS: MoErging through Adaptive Subspace Selection
Donato Crisostomi, Alessandro Zirilli, Antonio Andrea Gargiulo +5
Model merging has recently emerged as a lightweight alternative to ensembling, combining multiple fine-tuned models into a single set of parameters with no additional training over…
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…
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…