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SUMMARY:ASSET Seminar: "Making Machine Learning Predictably Reliable" (Andrew Ilyas\, Massachusetts Institute of Technology)
DESCRIPTION:ABSTRACT:  \nDespite ML models’ impressive performance\, training and deploying them is currently a somewhat messy endeavor. But does it have to be? In this talk\, I overview my work on making ML “predictably reliable”—enabling developers to know when their models will work\, when they will fail\, and why. \nTo begin\, we use a case study of adversarial inputs to show that human intuition can be a poor predictor of how ML models operate. Motivated by this\, we present a line of work that aims to develop a precise understanding of the ML pipeline\, combining statistical tools with large-scale experiments to characterize the role of each individual design choice: from how to collect data\, to what dataset to train on\, to what learning algorithm to use. \nZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/98699592762
URL:https://seasevents.nmsdev7.com/event/asset-seminar-andrew-ilyas-massachusetts-institute-of-technology/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
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