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 | 10314 | 2006 |
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation SJ Phillips, M Dudík Ecography 31 (2), 161-175, 2008 | 8420 | 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 | 7741 | 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 | 3305 | 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 | 3242 | 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 | 2458 | 2017 |
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 | 1375* | 2017 |
A reductions approach to fair classification A Agarwal, A Beygelzimer, M Dudík, J Langford, H Wallach ICML 2018, 2018 | 1285 | 2018 |
Doubly robust policy evaluation and learning M Dudik, J Langford, L Li ICML 2011, 2011 | 917 | 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 | 916 | 2019 |
Doubly robust policy evaluation and optimization M Dudík, D Erhan, J Langford, L Li | 472 | 2014 |
A reliable effective terascale linear learning system A Agarwal, O Chapelle, M Dudik, J Langford Journal of Machine Learning Research 15, 2014 | 449 | 2014 |
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 | 448 | 2020 |
Efficient Optimal Learning for Contextual Bandits M Dudik, D Hsu, S Kale, N Karampatziakis, J Langford, L Reyzin, T Zhang UAI 2011, 2011 | 374 | 2011 |
Fair Regression: Quantitative Definitions and Reduction-based Algorithms A Agarwal, M Dudík, ZS Wu ICML 2019, 2019 | 320 | 2019 |
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 | 318 | 2005 |
Performance guarantees for regularized maximum entropy density estimation M Dudik, SJ Phillips, RE Schapire International Conference on Computational Learning Theory, 472-486, 2004 | 316 | 2004 |
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 | 281 | 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 | 279 | 2019 |
Hierarchical imitation and reinforcement learning HM Le, N Jiang, A Agarwal, M Dudík, Y Yue, H Daumé III ICML 2018, 2018 | 239 | 2018 |