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Found 3 papers, 1 papers with code
Scalable Whitebox Attacks on Tree-based Models
Adversarial robustness is one of the essential safety criteria for guaranteeing the reliability of machine learning models.
PUMA: Performance Unchanged Model Augmentation for Training Data Removal
It then complements the negative impact of removing marked data by reweighting the remaining data optimally.
PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards
We propose a model criticism and explanation framework based on adversarially generated counterfactual examples for tabular data.
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