9 citations · 17 across the 13 of their papers we have counts for
10 papers · 1 filter
eval-unlearn: Benchmarking unlearning in Text-to-Image Diffusion Models
Mansi, Nikhil Raghavan, Zixia Huang +4
The rising number of concept unlearning techniques for text-to-image (T2I) diffusion models has produced a fragmented evaluation landscape. Methods are assessed under heterogeneous…
Certifying Concept Unlearning in Text-to-Image Diffusion Models
Mansi, Luca Marzari, Francesco Leofante
Existing evaluations of concept unlearning in text-to-image (T2I) diffusion models primarily rely on attack success rates obtained through automated adversarial prompt search. Howe…
Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders
Junqi Jiang, Francesco Leofante, Antonio Rago +1
Counterfactual explanations (CEs) provide recourse recommendations for individuals affected by algorithmic decisions. A key challenge is generating CEs that are robust against vari…
Argumentative Ensembling for Robust Recourse under Model Multiplicity
Junqi Jiang, Antonio Rago, Francesco Leofante +1
In machine learning, it is common to obtain multiple equally performing models for the same prediction task, e.g., when training neural networks with different random seeds. Model…
RobustX: Robust Counterfactual Explanations Made Easy
Junqi Jiang, Luca Marzari, Aaryan Purohit +1
The increasing use of Machine Learning (ML) models to aid decision-making in high-stakes industries demands explainability to facilitate trust. Counterfactual Explanations (CEs) ar…
Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
Luca Marzari, Francesco Leofante, Ferdinando Cicalese +1
We study the problem of assessing the robustness of counterfactual explanations for deep learning models. We focus on altering model parameters an…
Interval Abstractions for Robust Counterfactual Explanations
Junqi Jiang, Francesco Leofante, Antonio Rago +1
Counterfactual Explanations (CEs) have emerged as a major paradigm in explainable AI research, providing recourse recommendations for users affected by the decisions of machine lea…
Robust Counterfactual Explanations in Machine Learning: A Survey
Junqi Jiang, Francesco Leofante, Antonio Rago +1
Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models. While…
Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)
Junqi Jiang, Antonio Rago, Francesco Leofante +1
Model Multiplicity (MM) arises when multiple, equally performing machine learning models can be trained to solve the same prediction task. Recent studies show that models obtained…
Promoting Counterfactual Robustness through Diversity
Francesco Leofante, Nico Potyka
Counterfactual explanations shed light on the decisions of black-box models by explaining how an input can be altered to obtain a favourable decision from the model (e.g., when a l…