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ASSET Seminar: “Making Machine Learning Predictably Reliable” (Andrew Ilyas, Massachusetts Institute of Technology)

March 13 at 12:00 PM - 1:30 PM


Despite 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.

To 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.

ZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/98699592762

Andrew Ilyas

Ph.D. Student

Andrew Ilyas is a PhD student in Computer Science at MIT, where he is advised by Aleksander Madry and Constantinos Daskalakis. His research aims to improve the reliability and predictability of machine learning systems. He was previously supported by an Open Philanthropy AI Fellowship.


March 13
12:00 PM - 1:30 PM
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Raisler Lounge (Room 225), Towne Building
220 South 33rd Street
Philadelphia, PA 19104 United States
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