138 citations · 597 across the 30 of their papers we have counts for
11 papers · 1 filter
When to Trust AI: Advances and Challenges for Certification of Neural Networks
Marta Kwiatkowska, Xiyue Zhang
Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis an…
Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality Guarantees
Daqian Shao, Marta Kwiatkowska
Linear Temporal Logic (LTL) is widely used to specify high-level objectives for system policies, and it is highly desirable for autonomous systems to learn the optimal policy with…
Certifiers Make Neural Networks Vulnerable to Availability Attacks
Tobias Lorenz, Marta Kwiatkowska, Mario Fritz
To achieve reliable, robust, and safe AI systems, it is vital to implement fallback strategies when AI predictions cannot be trusted. Certifiers for neural networks are a reliable…
Statistical Guarantees for the Robustness of Bayesian Neural Networks
Luca Cardelli, Marta Kwiatkowska, Luca Laurenti +3
We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set…
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang, Daniel Kroening, Wenjie Ruan +5
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deplo…
Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control
Rhiannon Michelmore, Marta Kwiatkowska, Yarin Gal
A rise in popularity of Deep Neural Networks (DNNs), attributed to more powerful GPUs and widely available datasets, has seen them being increasingly used within safety-critical do…