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SUMMARY:ASSET Seminar: "Building a Foundation for Trustworthy Machine Learning" (Elan Rosenfeld\, Carnegie Mellon University)
DESCRIPTION:ABSTRACT: \n\n\nArtificial Intelligence is being increasingly relied on in safety-critical domains. But the predictive models underlying these systems are notoriously brittle\, and trustworthy deployment remains a significant challenge. In this talk\, I give an overview of my work towards a rigorous foundation for robust machine learning (ML).\n\n\nUsing a case study of invariant prediction\, we first highlight the importance of formally specifying the space of adverse events we’d like to handle at deployment time. This provides a mathematical framework for analyzing\, comparing\, and improving the robustness of ML algorithms. Then\, we explore how careful experimental probing of these methods’ failures leads to a deeper understanding of the underlying causes\, and how these insights can inform the design of new methods with more reliable real-world behavior. We conclude with a brief summary of other past and ongoing works towards provably secure ML\, including a scalable framework which enables certified robustness to adversarial train- and test-time attacks. \n\nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/95678270617
URL:https://seasevents.nmsdev7.com/event/asset-seminar-elan-rosenfeld-carnegie-mellon-university/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
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