Abstract:
In this talk, we will focus on the recently-emerged field of (adversarially) robust learning. This field began by the observation that modern learning models, despite the breakthrough performance, remain fragile to seemingly innocuous changes in the data such as small, norm-bounded perturbations of the input data. In response, various training methodologies have been developed for enhancing robustness. However, it is fair to say that our understanding in this field is still at its infancy and several key questions remain widely open. We will consider two such questions.
(1) Fundamental limits: It has been repeatedly observed that improving robustness to perturbed inputs (robust accuracy) comes at the cost of decreasing the accuracy of benign inputs (standard accuracy), leading to a fundamental tradeoff between these often competing objectives. Complicating matters further, recent empirical evidence suggests that a variety of other factors (size and quality of training data, model size, etc.) affect this tradeoff in somewhat surprising ways. In the first part of the talk, we will develop a precise and comprehensive understanding of such tradeoffs in the context of the simple yet foundational problem of linear regression.
(2) Robustness to other types of out-of-distribution inputs: There are other sources of fragility for deep learning that are arguably more common and less studied. Indeed, natural variation such as lighting or weather conditions or device imperfections can significantly degrade the accuracy of trained neural networks, proving that such natural variation presents a significant challenge. To this end, in the second part of the talk, we propose a paradigm shift from perturbation-based adversarial robustness toward a new framework called “model-based robust deep learning”. Using this framework, we will provide general training algorithms that improve the robustness of neural networks against natural variation in data. We will show the success of this framework to improve robustness of modern learning models consistently against many types of natural out-of-distribution inputs and across a variety of commonly-used datasets.
Bio:
Hamed Hassani is currently an assistant professor of Electrical and Systems Engineering department at the University of Pennsylvania. Prior to that, he was a research fellow at Simons Institute for the Theory of Computing (UC Berkeley) affiliated with the program of Foundations of Machine Learning, and a post-doctoral researcher in the Institute of Machine Learning at ETH Zurich. He received a Ph.D. degree in Computer and Communication Sciences from EPFL, Lausanne. He is the recipient of the 2014 IEEE Information Theory Society Thomas M. Cover Dissertation Award, 2015 IEEE International Symposium on Information Theory Student Paper Award, 2017 Simons-Berkeley Fellowship, 2018 NSF-CRII Research Initiative Award, 2020 Air Force Office of Scientific Research (AFOSR) Young Investigator Award, and 2020 National Science Foundation (NSF) CAREER Award.
Access:
Seminar will be delivered live via Zoom (link is external)(sign-in required) on Friday, September 11, 11:00 AM – 12:00 PM (CDT; UTC -5)
The Zoom conferencing system is accessible to UT faculty, staff, and students(link is external) with support from ITS. Otherwise, you can sign up for a free account on the Zoom website(link is external).
