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PublicationSafeguarding Learning-based Control for Smart Energy Systems with Sampling Specifications( 2023)We study challenges using reinforcement learning in controlling energy systems, where apart from performance requirements, one has additional safety requirements such as avoiding blackouts. We detail how these safety requirements in real-time temporal logic can be strengthened via discretization into linear temporal logic (LTL), such that the satisfaction of the LTL formulae implies the satisfaction of the original safety requirements. The discretization enables advanced engineering methods such as synthesizing shields for safe reinforcement learning as well as formal verification, where for statistical model checking, the probabilistic guarantee acquired by LTL model checking forms a lower bound for the satisfaction of the original real-time safety requirements.
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PublicationStatistical Property Testing for Generative Models( 2023)Generative models that produce images, text, or other types of data are recently be equipped with more powerful capabilities. Nevertheless, in some use cases of the generated data (e.g., using it for model training), one must ensure that the synthetic data points satisfy some properties that make them suitable for the intended use. Towards this goal, we present a simple framework to statistically check if the data produced by a generative model satisfy some property with a given confidence level. We apply our methodology to standard image and text-to-image generative models.
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PublicationStatistical Guarantees for Safe 2D Object Detection Post-processing( 2023)Safe and reliable object detection is essential for safetycritical applications of machine learning, such as autonomous driving. However, standard object detection methods cannot guarantee their performance during operation. In this work, we leverage conformal prediction in order to provide statistical guarantees for back-box object detection models. Extending prior work, we present a postprocessing methodology that can cover the entire object detection problem (localization, classification, false negatives, detection in videos, etc.), while offering sound safety guarantees on its error rates. We apply our method on state-of-the-art 2D object detection models and measure its efficacy in practice. Moreover, we investigate what happens as the acceptable error rates are pushed towards high safety levels. Overall, the presented methodology offers a practical approach towards safety-aware object detection, and we hope it can pave the way for further research in this area.
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PublicationCan Conformal Prediction Obtain Meaningful Safety Guarantees for ML Models?( 2023)Conformal Prediction (CP) has been recently proposed as a methodology to calibrate the predictions of Machine Learning (ML) models so that they can output rigorous quantification of their uncertainties. For example, one can calibrate the predictions of an ML model into prediction sets, that guarantee to cover the ground truth class with a probability larger than a specified threshold. In this paper, we study whether CP can provide strong statistical guarantees that would be required in safety-critical applications. Our evaluation on the ImageNet demonstrates that using CP over state-of-the-art models fails to deliver the required guarantees. We corroborate our results by deriving a simple connection between the CP prediction sets and top-k accuracy.
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PublicationDeutsche Normungsroadmap Künstliche Intelligenz(DIN, 2022-12-09)
