Learning-Augmented Approach Improves Robust Algorithmic Recourse

A new learning‑augmented approach improves the robustness of algorithmic recourse by integrating extra data to enhance feasible counterfactuals, the authors report. Read more: getnews.me/learning-augmented-appro... #algorithmicrecourse #machinelearning

Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures Counterfactual explanations indicate the smallest change in input that can translate to a different outcome for a machine learning model. Counterfactuals have generated immense interest in high-stakes...

🔈 Sharing our recent paper on "Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures" 🎉 🎉 Accepted at #AAMAS2025
Joint work with: Erfaun Noorani Pasan Dissanayake Faisal Hamman #Explainability #XAI #AlgorithmicRecourse #EntropicRisk arxiv.org/abs/2503.07934