Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
- Steven J. Phillips, Miroslav Dudík
- Computer Science
- 1 April 2008
This paper presents a tuning method that uses presence-only data for parameter tuning, and introduces several concepts that improve the predictive accuracy and running time of Maxent and describes a new logistic output format that gives an estimate of probability of presence.
Novel methods improve prediction of species' distributions from occurrence data
- J. Elith, C. Graham, N. Zimmermann
- Environmental Science
- 1 April 2006
This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
A statistical explanation of MaxEnt for ecologists
- J. Elith, Steven J. Phillips, T. Hastie, Miroslav Dudík, Y. Chee, C. Yates
- Environmental Science
- 1 January 2011
A new statistical explanation of MaxEnt is described, showing that the model minimizes the relative entropy between two probability densities defined in covariate space, which is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts.
A maximum entropy approach to species distribution modeling
- Steven J. Phillips, Miroslav Dudík, R. Schapire
- Computer ScienceInternational Conference on Machine Learning
- 4 July 2004
This work proposes the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features, and investigates the interpretability of models constructed using maxent.
Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.
- Steven J. Phillips, Miroslav Dudík, S. Ferrier
- Environmental ScienceEcological Applications
- 2009
It is argued that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions and as large an effect on predictive performance as the choice of modeling method.
Opening the black box: an open-source release of Maxent
- Steven J. Phillips, Robert P. Anderson, Miroslav Dudík, R. Schapire, M. Blair
- Computer Science
- 1 July 2017
A new open-source release of the Maxent software for modeling species distributions from occurrence records and environmental data is announced, and a new R package for fitting Maxent models using the glmnet package for regularized generalized linear models is described.
A Reductions Approach to Fair Classification
- Alekh Agarwal, A. Beygelzimer, Miroslav Dudík, J. Langford, H. Wallach
- Computer ScienceInternational Conference on Machine Learning
- 6 March 2018
The key idea is to reduce fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints.
Doubly Robust Policy Evaluation and Learning
- Miroslav Dudík, J. Langford, Lihong Li
- Computer ScienceInternational Conference on Machine Learning
- 23 March 2011
It is proved that the doubly robust approach uniformly improves over existing techniques, achieving both lower variance in value estimation and better policies, and is expected to become common practice.
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
- Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, Miroslav Dudík, H. Wallach
- Computer ScienceInternational Conference on Human Factors in…
- 13 December 2018
This first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems identifies areas of alignment and disconnect between the challenges faced by teams in practice and the solutions proposed in the fair ML research literature.
A reliable effective terascale linear learning system
- Alekh Agarwal, O. Chapelle, Miroslav Dudík, J. Langford
- Computer ScienceJournal of machine learning research
- 19 October 2011
We present a system and a set of techniques for learning linear predictors with convex losses on terascale data sets, with trillions of features, billions of training examples and millions of…
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