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Upcoming Talks

(6/30/2022) Speaker: Shibani Santurkar

Stanford University

Actionably interpretable ML
Machine learning models today attain impressive accuracy on benchmark tasks. But as we move towards deploying these models in the real world, it becomes increasingly important to verify that they not only make the *right prediction*, but that they do so for the *right reasons*. The scale and complexity of current models however presents a major roadblock in achieving this goal.
In this talk, I will discuss a methodology to design neural networks that are accurate, yet at the same time inherently more debuggable. As we demonstrate via numerical and human experiments, our approach yields vision and language models wherein one can more easily pinpoint learned spurious correlations, explain misclassifications, and diagnose biases.
Shibani Santurkar is a postdoctoral researcher at Stanford University with Tatsu Hashimoto, Percy Liang and Tengyu Ma. Her research revolves around developing machine learning models that can perform reliably in the real world, and characterizing the consequences if they fail to do so. Shibani received a PhD in Computer Science from MIT in 2021, where she was advised by Aleksander Mądry and Nir Shavit. Prior to that, she obtained a B.Tech and M.Tech in electrical engineering from the Indian Institute of Technology Bombay. She is a recipient of the Google Fellowship and an Open Philanthropy early-career grant.
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