John Langford studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his Ph.D. from Carnegie Mellon University in 2002. Since then, he has worked at Yahoo!, Toyota Technological Institute, and IBM's Watson Research Center. He is also the primary author of the popular Machine Learning weblog, hunch.net and the principle developer of Vowpal Wabbit. Previous research projects include Isomap, Captcha, Learning Reductions, Cover Trees, and Contextual Bandit learning. For more information visit http://hunch.net/~jl.
- Tzu-Kuo Huang, Alekh Agarwal, Daniel Hsu, John Langford, and Robert Schapire, Efficient and Parsimonious Agnostic Active Learning, December 2015.
- Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal III, and John Langford, Learning to Search Better than Your Teacher, in Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), July 2015.
- Nicolas S. Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, Yoav Shoham, and David M. Pennock, An Axiomatic Characterization of Wagering Mechanisms, in Journal of Economic Theory, vol. 156, pp. 389-416, March 2015.
- Alekh Agarwal, Alina Beygelzimer, Daniel Hsu, John Langford, and Matus Telgarsky, Scalable Nonlinear Learning with Adaptive Polynomial Expansions, December 2014.
- Miroslav Dudik, Dumitru Erhan, John Langford, and Lihong Li, Doubly Robust Policy Evaluation and Optimization, in Statistical Science, Institute of Mathematical Statistics, November 2014.
- Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert E. Schapire, Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits, in The 31st International Conference on Machine Learning (ICML 2014), JMLR: Workshop and Conference Proceedings, June 2014.
- Ashwinkumar Badanidiyuru, John Langford, and Aleksandrs Slivkins, Resourceful Contextual Bandits, in 27th Conf. on Learning Theory (COLT), 2014.
- Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, and John Langford, Efficient Online Bootstrapping for Large Scale Learning, no. MSR-TR-2013-132, December 2013.