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This paper rethinks structural anomaly detection by shifting from decision boundaries to projection operators onto the low-dimensional manifold of normal data, showing that projection-aligned methods outperform existing boundary-based and reconstruction-based approaches.
This paper examines counterfactual behavior in ML models through a geometric lens, showing that models with similar predictive performance can differ substantially in counterfactual outcomes due to the interaction between decision-boundary proximity and local data support. The findings identify counterfactual behavior as a distinct dimension from predictive performance, with implications for model selection and reliability of counterfactual explanation methods.