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SUMMARY:CIS Seminar: "Building the Reliability Stack for Machine Learning"
DESCRIPTION:Currently\, machine learning (ML) systems have impressive performance but can behave in unexpected ways. These systems latch onto unintuitive patterns and are easily compromised\, a source of grave concern for deployed ML in settings such as healthcare\, security\, and autonomous driving. In this talk\, I will discuss how we can redesign the core ML pipeline to create reliable systems. First\, I will show how to train provably robust models\, which enables formal robustness guarantees for complex deep networks. Next\, I will demonstrate how to make ML models more debuggable. This amplifies our ability to diagnose failure modes\, such as hidden biases or spurious correlations. To conclude\, I will discuss how we can build upon this “reliability stack” to enable broader robustness requirements\, and develop new primitives that make ML debuggable by design.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-building-the-reliability-stack-for-machine-learning/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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