Rob Schapire is a Principal Researcher at Microsoft Research in New York City. He received his PhD from MIT in 1991. After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly AT&T Bell Laboratories) in 1991. In 2002, he became a Professor of Computer Science at Princeton University. He joined Microsoft Research in 2014. His awards include the 1991 ACM Doctoral Dissertation Award, the 2003 Gödel Prize, and the 2004 Kanelakkis Theory and Practice Award (both of the last two with Yoav Freund). He is a fellow of the AAAI, and a member of the National Academy of Engineering. His main research interest is in theoretical and applied machine learning, with particular focus on boosting, online learning, game theory, and maximum entropy.
For more information, see http://rob.schapire.net.
- Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, and Robert Schapire, Fast Convergence of Regularized Learning in Games, December 2015.
- Tzu-Kuo Huang, Alekh Agarwal, Daniel Hsu, John Langford, and Robert Schapire, Efficient and Parsimonious Agnostic Active Learning, December 2015.
- Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, and Masrour Zoghi, Contextual Dueling Bandits, in Proceedings of The 28th Conference on Learning Theory (COLT), July 2015.