6 papers
Randomized Smoothing Meets Vision-Language Models
Emmanouil Seferis, Changshun Wu, Stefanos Kollias +2
Randomized smoothing (RS) is one of the prominent techniques to ensure the correctness of machine learning models, where point-wise robustness certificates can be derived analytica…
Estimating the Robustness Radius for Randomized Smoothing with 100 Sample Efficiency
Emmanouil Seferis, Stefanos Kollias, Chih-Hong Cheng
Randomized smoothing (RS) has successfully been used to improve the robustness of predictions for deep neural networks (DNNs) by adding random noise to create multiple variations o…
Facilitating Change Implementation for Continuous ML-Safety Assurance
Chih-Hong Cheng, Nguyen Anh Vu Doan, Balahari Balu +9
We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering pr…
Prioritizing Corners in OoD Detectors via Symbolic String Manipulation
Chih-Hong Cheng, Changshun Wu, Emmanouil Seferis +1
For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training d…
SMC4PEP: Stochastic Model Checking of Product Engineering Processes
Hassan Hage, Emmanouil Seferis, Vahid Hashemi +1
Product Engineering Processes (PEPs) are used for describing complex product developments in big enterprises such as automotive and avionics industries. The Business Process Model…
Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection
Tobias Schuster, Emmanouil Seferis, Simon Burton +1
In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations: th…