@article(balaji2018benchmarking, author = {Adithya Balaji and Alexander Allen}, year = {2018}, title = {Benchmarking Automatic Machine Learning Frameworks}, journal = {ArXiv}, volume = {abs/1808.06492}, ) @article(Chen_2016, author = {Tianqi Chen and Carlos Guestrin}, year = {2016}, title = {XGBoost}, journal = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, doi = {10.1145/2939672.2939785}, ) @inbook(Feurer2019, author = {Matthias Feurer and Aaron Klein and Katharina Eggensperger and Jost~Tobias Springenberg and Manuel Blum and Frank Hutter}, year = {2019}, title = {Auto-sklearn: Efficient and Robust Automated Machine Learning}, pages = {113--134}, publisher = {Springer International Publishing}, address = {Cham}, doi = {10.1007/978-3-030-05318-5\_6}, ) @article(gijsbers2019open, author = {P.~J.~A. Gijsbers and Erin LeDell and Janek Thomas and S'ebastien Poirier and Bernd Bischl and Joaquin Vanschoren}, year = {2019}, title = {An Open Source AutoML Benchmark}, journal = {ArXiv}, volume = {abs/1907.00909}, ) @manual(H2OAutoML, author = {H2O.ai}, year = {2017}, title = {H2O AutoML}, url = {http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html}, note = {H2O version 3.30.0.1}, ) @misc(repo, author = {Tuomas Halvari}, title = {{AutoML comparison}}, url = {https://github.com/thalvari/AutoML_comparison}, ) @inproceedings(jin2019auto, author = {Haifeng Jin and Qingquan Song and Xia Hu}, year = {2019}, title = {Auto-Keras: An Efficient Neural Architecture Search System}, booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining}, series = {KDD \IeC{\textquoteright}19}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, pages = {1946\IeC{\textendash}1956}, doi = {10.1145/3292500.3330648}, ) @article(super-learner, author = {Mark~J. van~der Laan and Eric~C Polley and Alan~E. Hubbard}, year = {2007}, title = {Super Learner}, journal = {Statistical Applications in Genetics and Molecular Biology}, volume = {6}, number = {1}, doi = {10.2202/1544-6115.1309}, url = {https://www.degruyter.com/view/journals/sagmb/6/1/article-sagmb.2007.6.1.1309.xml.xml}, ) @article(aleks2017deep, author = {Aleksander Madry and Aleksandar Makelov and Ludwig Schmidt and Dimitris Tsipras and Adrian Vladu}, year = {2018}, title = {Towards Deep Learning Models Resistant to Adversarial Attacks}, journal = {ArXiv}, volume = {abs/1706.06083}, ) @inproceedings(moosavidezfooli2015deepfool, author = {S.~{Moosavi-Dezfooli} and A.~{Fawzi} and P.~{Frossard}}, year = {2016}, title = {DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks}, booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, pages = {2574--2582}, doi = {10.1109/CVPR.2016.282}, ) @inproceedings(Nurminen2019, author = {{Jukka K} Nurminen and Tuomas Halvari and Juha Harviainen and Juha Myll{\"a}ri and Antti R{\"o}ysk{\"o} and Juuso Silvennoinen and Tommi Mikkonen}, year = {2019}, title = {Software Framework for Data Fault Injection to Test Machine Learning Systems}, booktitle = {Proceedings of 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE 2019) Workshops}, publisher = {IEEE}, pages = {294--299}, doi = {10.1109/ISSREW.2019.00087}, ) @inproceedings(OlsonGECCO2016, author = {Randal~S. Olson and Nathan Bartley and Ryan~J. Urbanowicz and Jason~H. Moore}, year = {2016}, title = {Evaluation of a Tree-Based Pipeline Optimization Tool for Automating Data Science}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference 2016}, series = {GECCO \IeC{\textquoteright}16}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, pages = {485\IeC{\textendash}492}, doi = {10.1145/2908812.2908918}, ) @article(scikit-learn, author = {Fabian Pedregosa and Ga\"{e}l Varoquaux and Alexandre Gramfort and Vincent Michel and Bertrand Thirion and Olivier Grisel and Mathieu Blondel and Peter Prettenhofer and Ron Weiss and Vincent Dubourg and Jake Vanderplas and Alexandre Passos and David Cournapeau and Matthieu Brucher and Matthieu Perrot and \'{E}douard Duchesnay}, year = {2011}, title = {Scikit-Learn: Machine Learning in Python}, journal = {J. Mach. Learn. Res.}, volume = {12}, number = {null}, pages = {2825\IeC{\textendash}2830}, doi = {10.5555/1953048.2078195}, ) @article(Shaham_2018, author = {Uri Shaham and Yutaro Yamada and Sahand Negahban}, year = {2018}, title = {Understanding adversarial training: Increasing local stability of supervised models through robust optimization}, journal = {Neurocomputing}, volume = {307}, pages = {195 -- 204}, doi = {10.1016/j.neucom.2018.04.027}, url = {http://www.sciencedirect.com/science/article/pii/S0925231218304557}, ) @inproceedings(truong2019towards, author = {A.~{Truong} and A.~{Walters} and J.~{Goodsitt} and K.~{Hines} and C.~B. {Bruss} and R.~{Farivar}}, year = {2019}, title = {Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools}, booktitle = {2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)}, pages = {1471--1479}, doi = {10.1109/ICTAI.2019.00209}, ) @inproceedings(Weng2019, author = {Tsui-Wei Weng and Pin-Yu Chen and Lam~M. Nguyen and Mark~S. Squillante and Akhilan Boopathy and Ivan Oseledets and Luca Daniel}, year = {2019}, title = {PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, series = {Proceedings of Machine Learning Research}, volume = {97}, pages = {6727--6736}, ) @article(xiao2017fashion, author = {Han Xiao and Kashif Rasul and Roland Vollgraf}, year = {2017}, title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms}, journal = {ArXiv}, volume = {abs/1708.07747}, )