2 papers
cs.LG2026
Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors
Niraj Kumar, Harsh Kasyap
Post-hoc model explainers such as LIME, SHAP, and Integrated Gradients are widely deployed to audit models in high-stakes sensitive domains, including finance, healthcare, and soci…
cs.LG2026
CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching
Jamie Heredge, Mattia J. Villani, Pranav Deshpande +2
Prior-fitted networks (PFNs) are a promising class of tabular foundation models that perform in-context learning, whereby the entire labelled training set is supplied as context, a…