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DTSTART;TZID=America/New_York:20220201T153000
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CREATED:20220125T183901Z
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SUMMARY:CIS Seminar: "Trustworthy Machine Learning Systems via PAC Uncertainty Quantification"
DESCRIPTION:Machine learning models are increasingly being incorporated into real-world systems\, targeting domains such as robotics\, healthcare\, and software systems. A key challenge is ensuring that such systems are trustworthy. I will describe a novel strategy for composing machine learning models while providing provable correctness guarantees. First\, we show how to quantify the uncertainty of any given model in a way that satisfies PAC correctness guarantees. Second\, we show how to compose guarantees for individual models to obtain a guarantee for the overall system. Then\, I will discuss applications to ensuring safety in reinforcement learning from visual inputs\, and to speeding up inference time of deep neural networks. I will conclude with ongoing work on preserving correctness guarantees in the face of distribution shift.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-trustworthy-machine-learning-systems-via-pac-uncertainty-quantification/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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