Miroslav Dudik
Miroslav Dudik
Microsoft Research
E-mailová adresa ověřena na:
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
A reductions approach to fair classification
A Agarwal, A Beygelzimer, M Dudík, J Langford, H Wallach
ICML 2018, 2018
Maxent software for modeling species niches and distributions v. 3.4.1
SJ Phillips, M Dudík, RE Schapire
URL:, 2017
Doubly robust policy evaluation and learning
M Dudik, J Langford, L Li
ICML 2011, 2011
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
A reliable effective terascale linear learning system
A Agarwal, O Chapelle, M Dudik, J Langford
Journal of Machine Learning Research 15, 2014
Doubly robust policy evaluation and optimization
M Dudík, D Erhan, J Langford, L Li
Fairlearn: A toolkit for assessing and improving fairness in AI
S Bird, M Dudík, R Edgar, B Horn, R Lutz, V Milan, M Sameki, H Wallach, ...
Microsoft, Tech. Rep. MSR-TR-2020-32, 2020
Efficient Optimal Learning for Contextual Bandits
M Dudik, D Hsu, S Kale, N Karampatziakis, J Langford, L Reyzin, T Zhang
UAI 2011, 2011
Performance guarantees for regularized maximum entropy density estimation
M Dudik, SJ Phillips, RE Schapire
International Conference on Computational Learning Theory, 472-486, 2004
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
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
A Agarwal, M Dudík, ZS Wu
ICML 2019, 2019
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
Provably efficient RL with rich observations via latent state decoding
SS Du, A Krishnamurthy, N Jiang, A Agarwal, M Dudík, J Langford
ICML 2019, 2019
Off-policy evaluation for slate recommendation
A Swaminathan, A Krishnamurthy, A Agarwal, M Dudik, J Langford, ...
Advances in Neural Information Processing Systems 30, 2017
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Články 1–20