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  • CS Colloquium: Andrew Ilyas - Making machine learning predictably reliable

    Thu, Mar 21, 2024 @ 10:00 AM - 11:00 AM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Andrew Ilyas, MIT

    Talk Title: Making machine learning predictably reliable

    Abstract: 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.   This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: 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.

    Host: Vatsal Sharan

    Location: Olin Hall of Engineering (OHE) - 136

    Audiences: Everyone Is Invited

    Contact: CS Faculty Affairs


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