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.
- 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
- 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
- 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
- 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
- Miroslav Dudik, Sebastien Lahaie, and David Pennock, A Tractable Combinatorial Market Maker Using Constraint Generation, in ACM Conference on Electronic Commerce, 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 Dudík, John Langford, and Lihong Li, Doubly Robust Policy Evaluation and Learning, in Proceedings of the Twenty-Eighth International Conference on Machine Learning (ICML-11), 2011
- Miroslav Dudik, Daniel Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, and Tong Zhang, Efficient optimal learning for contextual bandits, in Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI-11), 2011