activity
20222025
most citedAttribution-based Explanations that Provide Recourse Cannot be Robust

7 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ML2025

Performative Validity of Recourse Explanations

Gunnar König, Hidde Fokkema, Timo Freiesleben +2

When applicants get rejected by an algorithmic decision system, recourse explanations provide actionable suggestions for how to change their input features to get a positive evalua…

stat.ML2025

Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions

Hidde Fokkema, Tim van Erven, Sara Magliacane

Machine learning is a vital part of many real-world systems, but several concerns remain about the lack of interpretability, explainability and robustness of black-box AI systems.…

math.OC2024

Online Newton Method for Bandit Convex Optimisation

Hidde Fokkema, Dirk van der Hoeven, Tor Lattimore +1

We introduce a computationally efficient algorithm for zeroth-order bandit convex optimisation and prove that in the adversarial setting its regret is at most $d^{3.5} \sqrt{n} \ma…

cs.LG2023

The Risks of Recourse in Binary Classification

Hidde Fokkema, Damien Garreau, Tim van Erven

Algorithmic recourse provides explanations that help users overturn an unfavorable decision by a machine learning system. But so far very little attention has been paid to whether…

stat.ML2022★ 7 cited

Attribution-based Explanations that Provide Recourse Cannot be Robust

Hidde Fokkema, Rianne de Heide, Tim van Erven

Different users of machine learning methods require different explanations, depending on their goals. To make machine learning accountable to society, one important goal is to get…