3 papers
cs.LG2026
SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
Maksim Anisimov, Francesco Belardinelli, Matthew Wicker
Safety guarantees are a prerequisite to the deployment of reinforcement learning (RL) agents in safety-critical tasks. Often, deployment environments exhibit non-stationary dynamic…
cs.LG2025
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.…
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…