Junqi Jiang

Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation

2023 · arXiv (Cornell University)

Counterfactual Explanations (CEs) have received increasing interest as a major methodology for explaining neural network classifiers. Usually, CEs for an input-output pair are defined as data points with minimum distance to the input that are …

RobustX: Robust Counterfactual Explanations Made Easy

2025 · arXiv (Cornell University)

The increasing use of Machine Learning (ML) models to aid decision-making in high-stakes industries demands explainability to facilitate trust. Counterfactual Explanations (CEs) are ideally suited for this, as they can offer insights into the predictions …