3 papers
cs.LG2026
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
Michal Moshkovitz, Suraj Srinivas, Lesia Semenova +7
Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice,…
cs.DB2025
Advancing Fact Attribution for Query Answering: Aggregate Queries and Novel Algorithms
Omer Abramovich, Daniel Deutch, Nave Frost +2
In this paper, we introduce a novel approach to computing the contribution of input tuples to the result of the query, quantified by the Banzhaf and Shapley values. In contrast to…
cs.CR2025
CaFA: Cost-aware, Feasible Attacks With Database Constraints Against Neural Tabular Classifiers
Matan Ben-Tov, Daniel Deutch, Nave Frost +1
This work presents CaFA, a system for Cost-aware Feasible Attacks for assessing the robustness of neural tabular classifiers against adversarial examples realizable in the problem…