6 papers
Certified Robustness to Data Poisoning in Gradient-Based Training
Philip Sosnin, Mark N. Müller, Maximilian Baader +2
Modern machine learning pipelines leverage large amounts of public data, making it infeasible to guarantee data quality and leaving models open to poisoning and backdoor attacks. P…
Provably Safe Model Updates
Leo Elmecker-Plakolm, Pierre Fasterling, Philip Sosnin +2
Safety-critical environments are inherently dynamic. Distribution shifts, emerging vulnerabilities, and evolving requirements demand continuous updates to machine learning models.…
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…
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…
Certification for Differentially Private Prediction in Gradient-Based Training
Matthew Wicker, Philip Sosnin, Igor Shilov +5
We study private prediction where differential privacy is achieved by adding noise to the outputs of a non-private model. Existing methods rely on noise proportional to the global…
Scaling Mixed-Integer Programming for Certification of Neural Network Controllers Using Bounds Tightening
Philip Sosnin, Calvin Tsay
Neural networks offer a computationally efficient approximation of model predictive control, but they lack guarantees on the resulting controlled system's properties. Formal certif…