Corinna Cortes
Corinna Cortes is a VP in Google Research, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Corinna spent more than ten years at AT&T Labs - Research, formerly AT&T Bell Labs, where she held a distinguished research position. Corinna's research work is well-known in particular for her contributions to the theoretical foundations of support vector machines (SVMs), for which she jointly with Vladimir Vapnik received the 2008 Paris Kanellakis Theory and Practice Award, and her work on data-mining in very large data sets for which she was awarded the AT&T Science and Technology Medal in the year 2000. Corinna received her MS degree in Physics from University of Copenhagen and joined AT&T Bell Labs as a researcher in 1989. She received her Ph.D. in computer science from the University of Rochester in 1993.
Corinna is also a competitive and a mother of two.
Authored Publications
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Understanding the Effects of Batching in Online Active Learning
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (2020)
Relative deviation learning bounds and generalization with unbounded loss functions
Spencer Greenberg
Annals of Mathematics and Artificial Intelligence (2019)
Adaptation Based on Generalized Discrepancy
Journal of Machine Learning Research, 20 (2019), pp. 1-30
Learning GANs and Ensembles Using Discrepancy
Ningshan Zhang
Ben Adlam
Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
Scott Yang
Eugen Hotaj
Ben Adlam
Charles Weill
Vitaly Kuznetsov
Vladimir Macko
Hanna Mazzawi
Ghassen Jerfel
Scott Yak
(2019)
