If you wish to cite HumpDay, thanks and maybe for now:
@electronic{cottonhumpday,
title = {{HumpDay Optimization Package}},
year = {2021},
author = {Peter Cotton},
url = {https://github.com/microprediction/humpday}
}
Although I have a book soonish:
@electronic{cottonbook,
title = {{Microprediction: Building an Open AI Network}},
year = {2022},
author = {Peter Cotton},
publisher = {MIT Press}
}
which motivates HumpDay and other packages.
This is how I currently cite auto-this-and-that. PR's welcome
In no particular order
PySOT
@inproceedings{Wang2019FastApproach,
title = {{Fast online object tracking and segmentation: A unifying approach}},
year = {2019},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
author = {Wang, Qiang and Zhang, Li and Bertinetto, Luca and Hu, Weiming and Torr, Philip H.S.},
volume = {2019-June},
doi = {10.1109/CVPR.2019.00142},
issn = {10636919}
}
Scikit-optimize
@misc{Timgates422020Scikit-optimize,
title = {{scikit-optimize}},
year = {2020},
booktitle = {GitHub},
author = {{Timgates42}}
}
Nevergrad
@article{Bennet2021Nevergrad,
title = {{Nevergrad}},
year = {2021},
journal = {ACM SIGEVOlution},
author = {Bennet, Pauline and Doerr, Carola and Moreau, Antoine and Rapin, Jeremy and Teytaud, Fabien and Teytaud, Olivier},
number = {1},
volume = {14},
doi = {10.1145/3460310.3460312}
}
DLIB:
@article{King2009Dlib-ml:Toolkit,
title = {{Dlib-ml: A machine learning toolkit}},
year = {2009},
journal = {Journal of Machine Learning Research},
author = {King, Davis E.},
volume = {10},
issn = {15324435}
}
Py-BOBYQA
@article{Cartis2019ImprovingSolvers,
title = {{Improving the flexibility and robustness of model-based derivative-free optimization solvers}},
year = {2019},
journal = {ACM Transactions on Mathematical Software},
author = {Cartis, Coralia and Fiala, Jan and Marteau, Benjamin and Roberts, Lindon},
number = {3},
volume = {45},
doi = {10.1145/3338517},
issn = {15577295}
}
NLOPT
@article{Kumar2016BenchmarkingIACOR,
title = {{Benchmarking NLopt and state-of-the-art algorithms for continuous global optimization via IACOR}},
year = {2016},
journal = {Swarm and Evolutionary Computation},
author = {Kumar, Udit and Soman, Sumit and {Jayadeva}},
volume = {27},
doi = {10.1016/j.swevo.2015.10.005},
issn = {22106502}
}
Ultraopt
@misc{Tang_UltraOpt,
author = {Qichun Tang},
title = {UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt},
month = January,
year = 2021,
doi = {10.5281/zenodo.4430148},
version = {v0.1.0},
publisher = {Zenodo},
url = {https://doi.org/10.5281/zenodo.4430148}
}
Auto-Sklearn
@article{Feurer2015EfficientLearning,
title = {{Efficient and Robust Automated Machine Learning}},
year = {2015},
journal = {Advances in Neural Information Processing Systems 28},
author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and Springenberg, Jost and Blum, Manuel and Hutter, Frank},
pages = {2944--2952},
url = {http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf},
issn = {10495258}
}
Autoxgboost
@inproceedings{autoxgboost,
title={Automatic Gradient Boosting},
author={Thomas, Janek and Coors, Stefan and Bischl, Bernd},
booktitle={International Workshop on Automatic Machine Learning at ICML},
year={2018}
}
AutoGluon
@article{agtabular,
title={AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data},
author={Erickson, Nick and Mueller, Jonas and Shirkov, Alexander and Zhang, Hang and Larroy, Pedro and Li, Mu and Smola, Alexander},
journal={arXiv preprint arXiv:2003.06505},
year={2020}
}
H20 AutoML
@Manual{h20_automl,
title = {H20 AutoMl},
author = {Arno Candel et al},
year = {2015},
url = {https://github.com/h2oai},
}
FLAML
@inproceedings{Liu2021AnModels,
title = {{An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models}},
year = {2021},
author = {Liu, Xueqing and Wang, Chi},
doi = {10.18653/v1/2021.acl-long.178}
}
AutoKeras
@inproceedings{Jin2019Auto-keras:System,
title = {{Auto-keras: An efficient neural architecture search system}},
year = {2019},
booktitle = {Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
author = {Jin, Haifeng and Song, Qingquan and Hu, Xia},
doi = {10.1145/3292500.3330648}
}
AutoXGBoost
@inproceedings{autoxgboost,
title={Automatic Gradient Boosting},
author={Thomas, Janek and Coors, Stefan and Bischl, Bernd},
booktitle={International Workshop on Automatic Machine Learning at ICML},
year={2018}
}
Oboe
@inproceedings{Yang2019OBoe:Selection,
title = {{OBoe: Collaborative filtering for automl model selection}},
year = {2019},
booktitle = {Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
author = {Yang, Chengrun and Akimoto, Yuji and Kim, Dae Won and Udell, Madeleine},
doi = {10.1145/3292500.3330909}
}
PyCaret
@electronic{pycaret,
title = {{PyCaret Python Package}},
year = {2020},
author = {Moez Ali and Antoni Baum},
url = {https://github.com/pycaret/pycaret}
}
LightAutoML
@electronic{lightautoml,
title = {{LightAutoML: AutoML Solution for a Large Financial Services Ecosystem}},
year = {Sep 2021},
author = {Anton Vakhrushev and Alexander Ryzhkov and Maxim Savchenko and Dmitry Simakov and Richin Damdinov and Alexander Tuzhilin}
url={https://arxiv.org/pdf/2109.01528.pdf}
}
TPOT
@inproceedings{Olson2016EvaluationScience,
title = {{Evaluation of a tree-based pipeline optimization tool for automating data science}},
year = {2016},
booktitle = {GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference},
author = {Olson, Randal S. and Bartley, Nathan and Urbanowicz, Ryan J. and Moore, Jason H.},
doi = {10.1145/2908812.2908918}
}
MLJAR
@electronic{mljar,
title = {{MLJar Supervised}},
url={https://github.com/mljar/mljar-supervised}
}
GAMA
@article{Gijsbers2019GAMA:Assistant,
title = {{GAMA: Genetic Automated Machine learning Assistant}},
year = {2019},
journal = {Journal of Open Source Software},
author = {Gijsbers, Pieter and Vanschoren, Joaquin},
number = {33},
volume = {4},
doi = {10.21105/joss.01132},
issn = {2475-9066}
}
HyperOpt-Sklearn
@inproceedings{Komer2014Hyperopt-Sklearn:Scikit-Learn,
title = {{Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn}},
year = {2014},
booktitle = {Proceedings of the 13th Python in Science Conference},
author = {Komer, Brent and Bergstra, James and Eliasmith, Chris},
doi = {10.25080/majora-14bd3278-006}
}