Miro Dudík's research focuses on combining theoretical and applied aspects of machine learning, statistics, convex optimization and algorithms. Most recently he has worked on contextual bandits, large-scale learning, tractable pricing of prediction markets, and learning with gauge regularization.
He received his PhD from Princeton in 2007. He is a co-creator of the MaxEnt package for modeling species distributions, which is used by biologists around the world to design national parks, model impacts of climate change, and discover new species.
- 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.
- Miroslav Dudik, Dumitru Erhan, John Langford, and Lihong Li, Doubly Robust Policy Evaluation and Optimization, in Statistical Science, Institute of Mathematical Statistics, November 2014.
- Miroslav Dudik, Rafael Frongillo, and Jennifer Wortman Vaughan, Market Making with Decreasing Utility for Information, July 2014.
- Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudik, Robert Schapire, and Aleksandrs Slivkins, Robust Multi-objective Learning with Mentor Feedback, in 27th Conf. on Learning Theory (COLT), 2014.
- Sebastien Lahaie, Miro Dudik, David Rothschild, and David Pennock, A Combinatorial Prediction Market for the U.S. Elections, ACM Conference on Electronic Commerce, June 2013.
- Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li, Sample-efficient Nonstationary-policy Evaluation for Contextual Bandits, in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI-12), 2012.
- Alekh Agarwal, Miroslav Dudik, Satyen Kale, John Langford, and Robert E. Schapire, Contextual bandit learning with predictable rewards, in Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012.
- Miroslav Dudik, Sebastien Lahaie, and David Pennock, A Tractable Combinatorial Market Maker Using Constraint Generation, in ACM Conference on Electronic Commerce, 2012.
- Miroslav Dudik, Zaid Harchaoui, and Jerome Malick, Lifted coordinate descent for learning with trace-norm regularization, in Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012.
- Zaid Harchaoui, Matthijs Douze, Mattis Paulin, Miroslav Dudik, and Jerome Malick, Large-scale image classification with trace-norm regularization, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR-12), 2012.