Electrical Engineering and Computer Science

Theory Seminar

Learning with Noisy Labels

Ambuj Tewari

Assistant Professor
University of Michigan
Friday, November 08, 2013
10:30am - 11:30am
3941 BBB

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About the Event

The study of binary classification problems with noisy labels has a long history. Soon after the introduction of the noise-free PAC model, Angluin and Laird (1988) proposed the random classification noise model where each label is flipped independently with some (small) probability before being revealed to the learner. In a separate thread, researchers have investigated the use of convex surrogates, such the hinge loss used by SVMs and the exponential loss used by Adaboost, for the non-convex 0-1 loss. Surprisingly, little attention has been paid to the study of convex surrogates under the random classification noise model. I will talk about two approaches for modifying a convex surrogate so that it still works when labels are noisy. The first approach is based on unbiased estimators of the surrogate loss. The second approach relies on a connection between learning with noisy labels and weighted 0-1 loss minimization. Our results lend theoretical justification to popular heuristics such as biased SVMs and weighted logistic regression. During the talk, I will point a few open problems of possible interest to the theoretical CS community: What to do when the unbiased estimator approach yields non-convex optimization problems? What to do when labels are adversarially, not randomly, flipped? (Based on joint work with N. Nagarajan, I. S. Dhillon and P. Ravikumar that's going to presented at NIPS 2013.)


Ambuj Tewari is with the Department of Statistics and the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. He has served on senior program committees of the conferences Algorithmic Learning Theory (ALT), Conference on Learning Theory (COLT), and Neural Information Processing Systems (NIPS). His papers have received the student paper award (2005) and the best paper award (2011) at COLT. He received his M.A. in Statistics (2005) and Ph.D. in Computer Science (2007) from the University of California at Berkeley where his advisor was Peter Bartlett. He was a research assistant professor in Toyota Technological Institute at Chicago (2008-2010), an assistant professor (part-time) in the Department of Computer Science, University of Chicago (2008-2010), and a post-doctoral fellow in the Institute for Computational Engineering and Sciences, University of Texas at Austin (2010-2012). He has also been a Visiting Researcher at Microsoft Research, Redmond. His research interests are in statistical learning theory, online learning, optimization, and reinforcement learning.

Additional Information

Sponsor: CSE

Open to: UM Only