3 citations · 13 across the 19 of their papers we have counts for
8 papers · 1 filter
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…
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…
Efficient Contrastive Explanations on Demand
Yacine Izza, Joao Marques-Silva
Recent work revealed a tight connection between adversarial robustness and restricted forms of symbolic explanations, namely distance-based (formal) explanations. This connection i…
SHAP scores fail pervasively even when Lipschitz succeeds
Olivier Letoffe, Xuanxiang Huang, Joao Marques-Silva
The ubiquitous use of Shapley values in eXplainable AI (XAI) has been triggered by the tool SHAP, and as a result are commonly referred to as SHAP scores. Recent work devised examp…
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation
Yacine Izza, Xuanxiang Huang, Antonio Morgado +3
The uses of machine learning (ML) have snowballed in recent years. In many cases, ML models are highly complex, and their operation is beyond the understanding of human decision-ma…
Towards trustable SHAP scores
Olivier Letoffe, Xuanxiang Huang, Joao Marques-Silva
SHAP scores represent the proposed use of the well-known Shapley values in eXplainable Artificial Intelligence (XAI). Recent work has shown that the exact computation of SHAP score…
The Pros and Cons of Adversarial Robustness
Yacine Izza, Joao Marques-Silva
Robustness is widely regarded as a fundamental problem in the analysis of machine learning (ML) models. Most often robustness equates with deciding the non-existence of adversarial…
Locally-Minimal Probabilistic Explanations
Yacine Izza, Kuldeep S. Meel, Joao Marques-Silva
Explainable Artificial Intelligence (XAI) is widely regarding as a cornerstone of trustworthy AI. Unfortunately, most work on XAI offers no guarantees of rigor. In high-stakes doma…