Prof. Aleksander Mądry, CSAIL, MIT
Abstract: 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.
Throughout, we illustrate how one can leverage relatively standard tools (e.g., crowdsourcing, image processing) to quantify the biases that we observe.
Based on joint works with Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Jacob Steinhardt, Dimitris Tsipras and Kai Xiao.
Speaker bio.: Prof. Mądry is the Director of the MIT Center for Deployable Machine Learning, the Faculty Lead of the CSAIL-MSR Trustworthy and Robust AI Collaboration, a Professor of Computer Science in the MIT EECS Department, and member of both CSAIL and the Theory of Computation group.
His research spans algorithmic graph theory, optimization and machine learning. In particular, I have a strong interest in building on the existing machine learning techniques to forge a decision-making toolkit that is reliable and well-understood enough to be safely and responsibly deployed in the real world.
Research lab website: http://madry-lab.ml/
This research meeting will be hosted remotely via Zoom.
Zoom link: https://mit.zoom.us/j/97149851234?pwd=czZGbWFhYU41MXlLUzh4ZVZVUmt1Zz09