Halt Fast! Early Stopping for Certified Robustness

2026-06-25

AI Summary

This work addresses the high computational cost and inflexibility of randomized smoothing (RS), which, despite offering rigorous robustness guarantees, requires a preset number of samples and is ill-suited for real-time or resource-constrained settings. The paper introduces anytime-valid robustness certification—a novel paradigm enabled by a meta-learning-based adaptive framework. A lightweight meta-learner predicts input-specific priors to guide a sequential estimation process that dynamically allocates sampling resources and supports early stopping. This approach preserves statistical validity while drastically reducing sampling complexity—by up to 20× compared to conventional RS—and enables risk-aware, tiered allocation of computation based on user-defined confidence thresholds. The method thus opens a new pathway toward efficient, real-time robustness certification in safety-critical applications.

Abstract

Randomized Smoothing (RS) provides rigorous robustness guarantees for neural networks without architectural constraints, yet its adoption is limited by extreme computational costs. Standard RS requires tens of thousands of model evaluations per input and forces practitioners to commit to fixed sample sizes a priori. In this work, we present a novel meta-learning framework for anytime-valid certified robustness that adaptively deploys computational resources. By using a lightweight meta-learner to predict image-specific priors for a sequential E-process, we achieve a 20-fold reduction in sample complexity compared to traditional methods while maintaining rigorous statistical guarantees. Beyond raw efficiency, we demonstrate how anytime-validity enables adaptively allocating compute based upon application-specific risk thresholds, a form of resource triage impossible under classic certification frameworks. That this is achievable while also providing similar certification performance demonstrates that our approach provides a pathway for real-time, safety-critical certification deployments.

Problem

• Randomized Smoothing
• Certified Robustness
• Computational Cost
• Sample Complexity
• Early Stopping

Innovation

• Randomized Smoothing
• anytime-valid certification
• meta-learning
• sequential E-process
• adaptive computation