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Interval Certifications for Multilayered Perceptrons via Lattice Traversal
Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris +1
In this work we present a rigorous theoretical framework to a foundational problem of AI safety, namely adversarial robustness. In particular, we show that the adversarial robustne…
Are Safety Guarantees in Neural Networks Safe? How to Compute Trustworthy Robustness Certifications
Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris +1
A primary challenge in AI safety is the existence of adversarial examples -- slightly distorted inputs that cause a neural network (NN) to misclassify. To mitigate this problem, re…
Towards Rigorous Explainability by Feature Attribution
Olivier Létoffé, Xuanxiang Huang, Joao Marques-Silva
For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such methods lack rigor and can mislea…
The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
Merkouris Papamichail, Konstantinos Varsos, Giorgos Flouris +1
Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representations involve integer constraints…