Monday / CS Seminar

January 29th / 12:00 PM

In Person / Mudd 3514

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.

Biography

Andrew Ilyas is a PhD student at MIT, advised by Constantinos Daskalakis and Aleksander Madry. His main interest is in reliable machine learning, where he seeks to understand the effects of the individual design choices involved in building ML models. He was previously supported by an Open Philanthropy AI Fellowship.

Research Interests/Area

Machine Learning