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cs.LG2026
Exact Certification of Data-Poisoning Attacks Using Mixed-Integer Programming
Philip Sosnin, Jodie Knapp, Fraser Kennedy +2
This work introduces a verification framework that provides both sound and complete guarantees for data poisoning attacks during neural network training. We formulate adversarial d…
cs.LG2025
Abstract Gradient Training: A Unified Certification Framework for Data Poisoning, Unlearning, and Differential Privacy
Philip Sosnin, Matthew Wicker, Josh Collyer +1
The impact of inference-time data perturbation (e.g., adversarial attacks) has been extensively studied in machine learning, leading to well-established certification techniques fo…