Miroslav Dudik
Miroslav Dudik
Microsoft Research
Verified email at microsoft.com
Cited by
Cited by
Novel methods improve prediction of species’ distributions from occurrence data
J Elith*, C H. Graham*, R P. Anderson, M Dudík, S Ferrier, A Guisan, ...
Ecography 29 (2), 129-151, 2006
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
SJ Phillips, M Dudík
Ecography 31 (2), 161-175, 2008
A statistical explanation of MaxEnt for ecologists
J Elith, SJ Phillips, T Hastie, M Dudík, YE Chee, CJ Yates
Diversity and distributions 17 (1), 43-57, 2011
A maximum entropy approach to species distribution modeling
SJ Phillips, M Dudík, RE Schapire
Proceedings of the twenty-first international conference on Machine learning, 83, 2004
Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data
SJ Phillips, M Dudík, J Elith, CH Graham, A Lehmann, J Leathwick, ...
Ecological applications 19 (1), 181-197, 2009
Opening the black box: An open‐source release of Maxent
SJ Phillips, RP Anderson, M Dudík, RE Schapire, ME Blair
Ecography 40 (7), 887-893, 2017
Doubly robust policy evaluation and learning
M Dudik, J Langford, L Li
ICML 2011, 2011
A reductions approach to fair classification
A Agarwal, A Beygelzimer, M Dudík, J Langford, H Wallach
ICML 2018, 2018
A reliable effective terascale linear learning system
A Agarwal, O Chapelle, M Dudik, J Langford
Journal of Machine Learning Research 15, 2014
Maxent software for modeling species niches and distributions v. 3.4.1
SJ Phillips, M Dudík, RE Schapire
URL: https://biodiversityinformatics.amnh.org/open_source/maxent, 2017
Performance guarantees for regularized maximum entropy density estimation
M Dudik, SJ Phillips, RE Schapire
International Conference on Computational Learning Theory, 472-486, 2004
Improving fairness in machine learning systems: What do industry practitioners need?
K Holstein, J Wortman Vaughan, H Daumé III, M Dudik, H Wallach
Proceedings of the 2019 CHI conference on human factors in computing systems …, 2019
Efficient Optimal Learning for Contextual Bandits
M Dudik, D Hsu, S Kale, N Karampatziakis, J Langford, L Reyzin, T Zhang
UAI 2011, 2011
Maximum entropy density estimation with generalized regularization and an application to species distribution modeling
M Dudík, SJ Phillips, RE Schapire
Journal of Machine Learning Research 8, 1217-1260, 2007
Correcting sample selection bias in maximum entropy density estimation
M Dudık, RE Schapire, SJ Phillips
Advances in neural information processing systems 17, 323-330, 2005
Doubly robust policy evaluation and optimization
M Dudík, D Erhan, J Langford, L Li
Statistical Science 29 (4), 485-511, 2014
Lifted coordinate descent for learning with trace-norm regularization
M Dudík, Z Harchaoui, J Malick
AISTATS 2012, 2012
Maxent software for species distribution modeling
SJ Phillips, M Dudík, RE Schapire
URL: https://www.cs.princeton.edu/schapire/maxent, 2005
Large-scale image classification with trace-norm regularization
Z Harchaoui, M Douze, M Paulin, M Dudik, J Malick
CVPR 2012, 2012
Optimal and adaptive off-policy evaluation in contextual bandits
YX Wang, A Agarwal, M Dudík
International Conference on Machine Learning, 3589-3597, 2017
The system can't perform the operation now. Try again later.
Articles 1–20