9 citations · 17 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
Counterfactual Scenarios for Automated Planning
Nicola Gigante, Francesco Leofante, Andrea Micheli
Counterfactual Explanations (CEs) are a powerful technique used to explain Machine Learning models by showing how the input to a model should be minimally changed for the model to…
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…
Representation Consistency for Accurate and Coherent LLM Answer Aggregation
Junqi Jiang, Tom Bewley, Salim I. Amoukou +4
Test-time scaling improves large language models' (LLMs) performance by allocating more compute budget during inference. To achieve this, existing methods often require intricate m…
Counterfactual Strategies for Markov Decision Processes
Paul Kobialka, Lina Gerlach, Francesco Leofante +3
Counterfactuals are widely used in AI to explain how minimal changes to a model's input can lead to a different output. However, established methods for computing counterfactuals t…
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…