3 citations · 7 across the 7 of their papers we have counts for
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cs.LG2024★ 3 cited
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Xi Xin, Giles Hooker, Fei Huang
The adoption of artificial intelligence (AI) across industries has led to the widespread use of complex black-box models and interpretation tools for decision making. This paper pr…
cs.LG2024
Longitudinal Counterfactuals: Constraints and Opportunities
Alexander Asemota, Giles Hooker
Counterfactual explanations are a common approach to providing recourse to data subjects. However, current methodology can produce counterfactuals that cannot be achieved by the su…
stat.ML2024★ 2 cited
Statistical Significance of Feature Importance Rankings
Jeremy Goldwasser, Giles Hooker
Feature importance scores are ubiquitous tools for understanding the predictions of machine learning models. However, many popular attribution methods suffer from high instability…