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Adversarial Robustness: Theory and Practice
J. Zico Kolter · Aleksander Madry

Mon Dec 03 05:30 AM -- 07:30 AM (PST) @ Room 220 CD

The recent push to adopt machine learning solutions in real-world settings gives rise to a major challenge: can we develop ML solutions that, instead of merely working “most of the time”, are truly reliable and robust? This tutorial will survey some of the key challenges in this context and then focus on the topic of adversarial robustness: the widespread vulnerability of state-of-the-art deep learning models to adversarial misclassification (aka adversarial examples). We will discuss the practical as well as theoretical aspects of this phenomenon, with an emphasis on recent verification-based approaches to establishing formal robustness guarantees. Our treatment will go beyond viewing adversarial robustness solely as a security question. In particular, we will touch on the role it plays as a regularizer and its relation to generalization.

Author Information

J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, explainable, and rigorous methods in deep learning. In addition, he has worked on a number of application areas, highlighted by work on sustainability and smart energy systems. He is the recipient of the DARPA Young Faculty Award, and best paper awards at KDD, IJCAI, and PESGM.

Aleksander Madry (MIT)

Aleksander Madry is the NBX Associate Professor of Computer Science in the MIT EECS Department and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 2011 and, prior to joining the MIT faculty, he spent some time at Microsoft Research New England and on the faculty of EPFL. Aleksander's research interests span algorithms, continuous optimization, science of deep learning and understanding machine learning from a robustness perspective. His work has been recognized with a number of awards, including an NSF CAREER Award, an Alfred P. Sloan Research Fellowship, an ACM Doctoral Dissertation Award Honorable Mention, and 2018 Presburger Award.

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