5 citations · 6 across the 19 of their papers we have counts for
14 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…
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…
Energy Guided smoothness to improve Robustness in Graph Classification
Farooq Ahmad Wani, Maria Sofia Bucarelli, Andrea Giuseppe Di Francesco +2
Graph Neural Networks (GNNs) are powerful at solving graph classification tasks, yet applied problems often contain noisy labels. In this work, we study GNN robustness to label noi…
Task Singular Vectors: Reducing Task Interference in Model Merging
Antonio Andrea Gargiulo, Donato Crisostomi, Maria Sofia Bucarelli +3
Task Arithmetic has emerged as a simple yet effective method to merge models without additional training. However, by treating entire networks as flat parameter vectors, it overloo…
ATM: Improving Model Merging by Alternating Tuning and Merging
Luca Zhou, Daniele Solombrino, Donato Crisostomi +3
Model merging has emerged as a cost-efficient approximation to multitask learning. Among merging strategies, task arithmetic is notable for its simplicity and effectiveness. In thi…
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…
Link Prediction with Physics-Inspired Graph Neural Networks
Andrea Giuseppe Di Francesco, Francesco Caso, Maria Sofia Bucarelli +1
The message-passing mechanism underlying Graph Neural Networks (GNNs) is not naturally suited for heterophilic datasets, where adjacent nodes often have different labels. Most solu…
A topological description of loss surfaces based on Betti Numbers
Maria Sofia Bucarelli, Giuseppe Alessio D'Inverno, Monica Bianchini +2
In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on g…