Miro Dudík
Dissertation
  • Maximum entropy density estimation and modeling geographic distributions of species, PhD thesis, Department of Computer Science, Princeton University, 2007, [pdf] [tech report link]
Peer-reviewed conferences, journals, and chapters
  • A tractable combinatorial market maker using constraint generation, with S. Lahaie and D. Pennock, ACM Conference on Electronic Commerce, 2012, [pdf]
  • Large-scale image classification with trace-norm regularization, with Z. Harchaoui, M. Douze, M. Paulin and J. Malick, IEEE Conference on Computer Vision and Pattern Recognition, 2012, [pdf]
  • Lifted coordinate descent for learning with trace-norm regularization, with Z. Harchaoui and J. Malick, International Conference on Artificial Intelligence and Statistics, 2012, [pdf]
  • Contextual bandit learning with predictable rewards, with A. Agarwal, S. Kale, J. Langford and R. Schapire, International Conference on Artificial Intelligence and Statistics, 2012, [pdf]
  • Doubly robust policy evaluation and learning, with J. Langford and L. Li, International Conference on Machine Learning, 2011, [pdf]
  • Efficient optimal learning for contextual bandits, with D. Hsu, S. Kale, N. Karampatziakis, J. Langford, L. Reyzin and T. Zhang, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, 2011, [pdf] [full version]
  • Modeling group negotiation: three approaches that can inform behavioral sciences, with N. Turan, G. Gordon and L. R. Weingart, Research on Managing Groups and Teams 14, 2011, 189-205, [publisher link]
  • Maximizing revenue in symmetric resource allocation systems when user utilities exhibit diminishing returns, with R. Zivan, P. Paruchuri and K. Sycara, International Conference on Autonomous Agents and Multiagent Systems, 2011, [pdf]
  • A statistical explanation of MaxEnt for ecologists, with J. Elith, S. J. Phillips et al., Diversity and Distributions 17, 2011, 43-57, [pdf]
  • Reducing untruthful manipulation in envy-free Pareto optimal resource allocation, with R. Zivan, S. Okamoto and K. Sycara, IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2010, [pdf]
  • First-order mixed integer linear programming, with G. J. Gordon and S. A. Hong, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009, [pdf]
  • A sampling-based approach to computing equilibria in succinct extensive-form games, with G. J. Gordon, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009, [pdf]
  • Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data, with S. J. Phillips, J. Elith et al., Ecological Applications 19:1, 2009, 181-197, [pdf]
  • Generative and discriminative learning with unknown labeling bias, with S. J. Phillips, Advances in Neural Information Processing Systems 21, 2009, [ps] [pdf]
  • Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation, with S. J. Phillips, Ecography 31:2, 2008, 161-175, [pdf]
  • Maximum entropy density estimation with generalized regularization and an application to species distribution modeling, with S. J. Phillips and R. E. Schapire, Journal of Machine Learning Research 8, 2007, 1217-1260, [pdf]
  • Hierarchical maximum entropy density estimation, with D. M. Blei and R. E. Schapire, International Conference on Machine Learning, 2007, [pdf] [video]
  • Maximum entropy distribution estimation with generalized regularization, with R. E. Schapire, Proceedings of the 19th Annual Conference on Learning Theory, 2006, 123-138, [pdf]
  • Novel methods improve prediction of species' distributions from occurrence data, with J. Elith, C. Graham et al., Ecography 29:2, 2006, 129-151, [pdf]
  • Correcting sample selection bias in maximum entropy density estimation, with R. E. Schapire and S. J. Phillips, Advances in Neural Information Processing Systems 18, 2006, [ps] [pdf]
  • Performance guarantees for regularized maximum entropy density estimation, with S. J. Phillips and R. E. Schapire, Proceedings of the 17th Annual Conference on Learning Theory, 2004, 472-486, [ps] [pdf]
  • A maximum entropy approach to species distribution modeling, with S. J. Phillips and R. E. Schapire, International Conference on Machine Learning, 2004, 655-662, [ps] [pdf]
  • Reconstruction from subsequences, with L. J. Schulman, J. Combinatorial Theory Series A 103, 2003, 337-348, [publisher link]
Last modified: Sep 12th, 2011