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VERSION:2.0
PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260430T031328Z
UID:Seminar-dept-1495@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Lutz Oettershagen:MAILTO:Lutz.Oettershagen@liverpool.ac.uk
DTSTART:20260512T130000
DTEND:20260512T140000
SUMMARY:School Seminar Series
DESCRIPTION:Changshun Wu: Trustworthy AI at Runtime: Out-of-Distribution and Robustness in Open Worlds\n\nModern deep learning systems often perform well in controlled training environments, but their reliability degrades after deployment in open-world settings, where two fundamental gaps emerge: an epistemic gap that leads to “I don’t know” failures on out-of-distribution inputs, and a stability gap that makes predictions brittle under perturbations. In this talk, I present a runtime perspective on trustworthy AI that addresses these two gaps through deployment-time mechanisms rather than training-time redesign. For the epistemic gap, I first introduce a lightweight runtime monitoring framework for out-of-distribution detection that requires no modification to model architectures or training procedures, yet provides effective detection and filtering of unreliable predictions in real-world autonomous driving scenarios. I then present a complementary approach that moves beyond external detection by introducing a mitigation strategy for overconfident predictions, which improves robustness to distribution shift and is compatible with standard vision architectures including YOLO, Faster R-CNN, and DETR. For the stability gap, I turn to the problem of prediction robustness under perturbations. We take a first step toward extending randomized smoothing beyond its classical classification setting into generative models, from a runtime perspective, to explore its role in improving predictive stability. All these results highlight runtime interventions as a useful perspective for studying trustworthiness in open-world deployment, including monitoring, mitigation, and certification.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=1495
LOCATION:Brodie Tower Room 106
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