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paper.bib
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paper.bib
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@inproceedings{Holmes1994,
author = {Holmes, G. and Donkin, A. and Witten, I.H.},
booktitle = {Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference},
doi = {10.1109/ANZIIS.1994.396988},
isbn = {0-7803-2404-8},
pages = {357--361},
publisher = {IEEE},
title = {{WEKA: {A} machine learning workbench}},
url = {http://ieeexplore.ieee.org/document/396988/},
year = {1994}
}
@article{Gressmann2018,
title = {Probabilistic supervised learning},
author = {Frithjof Gressmann and Franz J. Király and Bilal Mateen and Harald Oberhauser},
year = {2018},
journal = {ArXiv},
volume = {1801.00753}
}
@article{Pedregosa2001,
author = {Pedregosa, Fabian and Varoquaux, Ga{\"{e}}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, {\'{E}}douard},
file = {:Users/mloning/Library/Application Support/Mendeley Desktop/Downloaded/Pedregosa et al. - 2001 - Scikit-learn Machine Learning in Python.pdf:pdf},
journal = {The Journal of Machine Learning Research},
pages = {2825--2830},
publisher = {MIT Press},
title = {{Scikit-learn: Machine Learning in Python}},
url = {https://dl.acm.org/doi/10.5555/1953048.2078195},
volume = {12},
year = {2011}
}
@article{Buitinck2013,
title = {{API} design for machine learning software: {e}xperiences from the scikit-learn project},
author = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and Fabian Pedregosa and Andreas Mueller and Olivier Grisel and Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort and Jaques Grobler and Robert Layton and Jacob VanderPlas and Arnaud Joly and Brian Holt and Ga{\"e}l Varoquaux},
journal = {ArXiv},
year = {2013},
volume = {abs/1309.0238}
}
@article{BischlEtal2016,
author = {Bernd Bischl and Michel Lang and Lars Kotthoff and Julia Schiffner and Jakob Richter and Erich Studerus and Giuseppe Casalicchio and Zachary M. Jones},
title = {{mlr: Machine Learning in R}},
journal = {Journal of Machine Learning Research},
year = {2016},
volume = {17},
number = {170},
pages = {1-5},
url = {http://jmlr.org/papers/v17/15-066.html}
}
@article{BezansonEtal2017,
author = {Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and
Shah, Viral B.},
title = {Julia: {A} fresh approach to numerical computing},
journal = {SIAM Rev.},
fjournal = {SIAM Review},
volume = {59},
year = {2017},
number = {1},
pages = {65--98},
issn = {0036-1445},
mrclass = {68N15 (65Y05 97P40)},
mrnumber = {3605826},
doi = {10.1137/141000671},
url = {https://doi.org/10.1137/141000671}
}
@article{Blaom_I,
author = {Blaom, Anthony},
title = {Flexible model composition in machine learning and its implementation in {MLJ}},
journal = {\normalfont {In preparation}},
year = {2020}
}
@article{Innes2018,
doi = {10.21105/joss.00602},
url = {https://doi.org/10.21105/joss.00602},
year = {2018},
publisher = {The Open Journal},
volume = {3},
number = {25},
pages = {602},
author = {Mike Innes},
title = {Flux: Elegant machine learning with {J}ulia},
journal = {Journal of Open Source Software}
}
@article{Kuhn2008,
author = {Max Kuhn},
title = {Building Predictive Models in {R} Using the caret Package},
journal = {Journal of Statistical Software, Articles},
volume = {28},
number = {5},
year = {2008},
keywords = {},
abstract = {The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.},
issn = {1548-7660},
pages = {1--26},
doi = {10.18637/jss.v028.i05},
url = {https://www.jstatsoft.org/v028/i05}
}
@software{LinEtal2020,
author = {Dahua Lin and
John Myles White and
Simon Byrne and
Andreas Noack and
Mathieu Besançon and
Douglas Bates and
John Pearson and
Alex Arslan and
Kevin Squire and
David Anthoff and
John Zito and
Theodore Papamarkou and
Moritz Schauer and
Jan Drugowitsch and
Avik Sengupta and
Brian J Smith and
Glenn Moynihan and
Giuseppe Ragusa and
Gord Stephen and
Christoph Dann and
Mike J Innes and
Michael and
Martin O'Leary and
Tamas K. Papp and
Jiahao Chen and
Iain Dunning and
Gustavo Lacerda and
Richard Reeve and
Kai Xu and
David Widmann},
title = {JuliaStats/Distributions.jl: {A} {J}ulia package for probability distributions and associated functions},
month = mar,
year = 2020,
publisher = {Zenodo},
version = {v0.23.2},
doi = {10.5281/zenodo.3730565},
url = {http://doi.org/10.5281/zenodo.3730565}
}
@misc{CategoricalArrays,
author = {Bouchet-Valat, Milan et al.},
title = {{CategoricalArrays.jl}: {A}rrays for working with categorical data},
year = {2014},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/JuliaData/CategoricalArrays.jl}
}
@misc{MLJ,
author = {Blaom, Anthony et al.},
title = {{MLJ: A machine learning framework for Julia}},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/alan-turing-institute/MLJ.jl}
}
@misc{MLJdocs,
author = {Blaom, Anthony},
title = {{MLJ} Documentation},
year = {2020},
publisher = {GitHub},
journal = {GitHub pages},
url = {https://alan-turing-institute.github.io/MLJ.jl/dev/}
}
@misc{MLJTuning,
author = {Blaom, Anthony and collaborators},
title = {Hyperparameter optimization algorithms for use in the {MLJ} machine learning framework},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/alan-turing-institute/MLJTuning.jl}
}
@misc{MLJtutorials,
author = {Lienart, Thibaut and Blaom, Anthony and collaborators},
title = {Data Science Tutorials in {J}ulia},
year = {2020},
publisher = {GitHub},
journal = {GitHub pages},
url = {https://alan-turing-institute.github.io/DataScienceTutorials.jl/}
}
@article{Rackauckas2017,
author = {Christopher Rackauckas},
journal = {The Winnower},
title = {A Comparison Between Differential Equation Solver Suites In MATLAB, R, Julia, Python, C, Mathematica, Maple, and Fortran},
year = {2018},
month = {08},
url = {https://dx.doi.org/10.15200/winn.153459.98975},
doi = {10.15200/winn.153459.98975}
}
@article{RackauckasNie2017,
author = {C. Rackauckas and Q. Nie},
year = {2017},
title = {{DifferentialEquations.jl – A} Performant and Feature-Rich Ecosystem for Solving Differential Equations in {J}ulia},
journal = {Journal of Open Research Software},
volume = 5,
number = 1,
pages = {15},
doi = {10.5334/jors.151},
url = {https://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/}
}
@misc{ScientificTypes,
author = {{Anthony Blaom and collaborators}},
title = {{ScientificTypes.jl: An API for dispatching on the "scientific" type of data instead of the machine type}},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/alan-turing-institute/ScientificTypes.jl}
}
@misc{Quinn,
author = {J. Quinn},
title = {Tables.jl: {A}n interface for tables in {J}ulia},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/JuliaData/Tables.jl}
}