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 | 7800 | 2006 |

Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation SJ Phillips, M Dudík Ecography 31 (2), 161-175, 2008 | 5595 | 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 | 4878 | 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 | 2431 | 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 | 2146 | 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 | 829 | 2017 |

Doubly robust policy evaluation and learning M Dudik, J Langford, L Li ICML 2011, 2011 | 425 | 2011 |

A reductions approach to fair classification A Agarwal, A Beygelzimer, M Dudík, J Langford, H Wallach ICML 2018, 2018 | 401 | 2018 |

A reliable effective terascale linear learning system A Agarwal, O Chapelle, M Dudik, J Langford Journal of Machine Learning Research 15, 2014 | 390 | 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 | 365* | 2017 |

Performance guarantees for regularized maximum entropy density estimation M Dudik, SJ Phillips, RE Schapire International Conference on Computational Learning Theory, 472-486, 2004 | 273 | 2004 |

Efficient Optimal Learning for Contextual Bandits M Dudik, D Hsu, S Kale, N Karampatziakis, J Langford, L Reyzin, T Zhang UAI 2011, 2011 | 243 | 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 | 242 | 2007 |

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 | 240 | 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 | 234 | 2005 |

Lifted coordinate descent for learning with trace-norm regularization M Dudík, Z Harchaoui, J Malick AISTATS 2012, 2012 | 125 | 2012 |

Doubly robust policy evaluation and optimization M Dudík, D Erhan, J Langford, L Li Statistical Science 29 (4), 485-511, 2014 | 124 | 2014 |

Large-scale image classification with trace-norm regularization Z Harchaoui, M Douze, M Paulin, M Dudik, J Malick CVPR 2012, 2012 | 123 | 2012 |

Maxent software for species distribution modeling SJ Phillips, M Dudík, RE Schapire URL: https://www.cs.princeton.edu/schapire/maxent, 2005 | 120* | 2005 |

Optimal and adaptive off-policy evaluation in contextual bandits YX Wang, A Agarwal, M Dudík International Conference on Machine Learning, 3589-3597, 2017 | 99 | 2017 |