| 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 | 10344 | 2006 |
| Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation SJ Phillips, M Dudík Ecography 31 (2), 161-175, 2008 | 8603 | 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 | 7960 | 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 | 3355 | 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 | 3297 | 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 | 2606 | 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 | 1405* | 2017 |
| A reductions approach to fair classification A Agarwal, A Beygelzimer, M Dudík, J Langford, H Wallach ICML 2018, 2018 | 1380 | 2018 |
| 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 | 990 | 2019 |
| Doubly robust policy evaluation and learning M Dudik, J Langford, L Li ICML 2011, 2011 | 943 | 2011 |
| 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 | 488 | 2020 |
| Doubly robust policy evaluation and optimization M Dudík, D Erhan, J Langford, L Li | 486 | 2014 |
| A reliable effective terascale linear learning system A Agarwal, O Chapelle, M Dudik, J Langford Journal of Machine Learning Research 15, 2014 | 458 | 2014 |
| Efficient Optimal Learning for Contextual Bandits M Dudik, D Hsu, S Kale, N Karampatziakis, J Langford, L Reyzin, T Zhang UAI 2011, 2011 | 383 | 2011 |
| Fair Regression: Quantitative Definitions and Reduction-based Algorithms A Agarwal, M Dudík, ZS Wu ICML 2019, 2019 | 345 | 2019 |
| Performance guarantees for regularized maximum entropy density estimation M Dudik, SJ Phillips, RE Schapire International Conference on Computational Learning Theory, 472-486, 2004 | 319 | 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 | 318 | 2005 |
| 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 | 286 | 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 | 284 | 2007 |
| Optimal and adaptive off-policy evaluation in contextual bandits YX Wang, A Agarwal, M Dudík International Conference on Machine Learning, 3589-3597, 2017 | 250 | 2017 |
