Certifying Ensembles: A General Certification Theory with S-Lipschitzness
Improving and guaranteeing the robustness of deep learning models has been a topic of intense research. Ensembling, which combines several classifiers to provide a better model, has shown to be beneficial for generalisation, uncertainty estimation, calibration, and mitigating the effects of concept drift. However, the impact of ensembling on certified robustness is less well understood. In this work, we generalise Lipschitz continuity by introducing S-Lipschitz classifiers, which we use to analyse the theoretical robustness of ensembles. Our results are precise conditions when ensembles of robust classifiers are more robust than any constituent classifier, as well as conditions when they are less robust.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Certifying Joint Adversarial Robustness for Model Ensembles
Deep Neural Networks (DNNs) are often vulnerable to adversarial examples.Several proposed defenses deploy an ensemble of models with the hope that, although the individual models may be vulnerable, an adversary will not …
Adversarial RobustnessmodelIncremental Randomized Smoothing Certification
Randomized smoothing-based certification is an effective approach for obtaining robustness certificates of deep neural networks (DNNs) against adversarial attacks. This method constructs a smoothed DNN model and certifie…
Certifying Out-of-Domain Generalization for Blackbox Functions
Certifying the robustness of model performance under bounded data distribution drifts has recently attracted intensive interest under the umbrella of distributional robustness. However, existing techniques either make st…
Domain GeneralizationThe Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis
Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act. In this context, the black-box nature of machine …
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing
As reinforcement learning (RL) has achieved great success and been even adopted in safety-critical domains such as autonomous vehicles, a range of empirical studies have been conducted to improve its robustness against a…
Atari GamesAutonomous Vehiclesreinforcement-learningReinforcement Learning+1