Theoretical Machine Learning Seminar

What Do Our Models Learn?

Large-scale vision benchmarks have driven---and often even defined---progress in machine learning. However, these benchmarks are merely proxies for the real-world tasks we actually care about. How well do our benchmarks capture such tasks?

In this talk, I will discuss the alignment between our benchmark-driven ML paradigm and the real-world uses cases that motivate it. First, we will explore examples of biases in the ImageNet dataset, and how state-of-the-art models exploit them. We will then demonstrate how these biases arise as a result of design choices in the data collection and curation processes.

Based on joint works with Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Jacob Steinhardt, Dimitris Tsipras and Kai Xiao.

Date & Time

June 09, 2020 | 12:30pm – 1:45pm

Location

Remote Access Only - see link below

Speakers

Aleksander Madry

Affiliation

Massachusetts Institute of Technology

Notes

We welcome broad participation in our seminar series. To receive login details, interested participants will need to fill out a registration form accessible from the link below.  Upcoming seminars in this series can be found here.

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