Bibliography

[BLB+13]

Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. API design for machine learning software: experiences from the scikit-learn project. In Proceedings of the Workshop Languages for Data Mining and Machine Learning @ ECMLPKDD 2013, 108–122. 2013. URL: https://dtai.cs.kuleuven.be/events/lml2013/papers/lml2013_api_sklearn.pdf.

[CLL+16]

Adrian Calma, Jan Marco Leimeister, Paul Lukowicz, Sarah Oeste-Reiss, Tobias Reitmaier, Albrecht Schmidt, Bernhard Sick, Gerd Stumme, and Katharina Anna Zweig. From Active Learning to Dedicated Collaborative Interactive Learning. In Proceedings of the International Conference on Architecture of Computing Systems, 1–8. 2016. URL: https://ieeexplore.ieee.org/document/7499239/.

[CS17]

Adrian Calma and Bernhard Sick. Simulation of Annotators for Active Learning: Uncertain Oracles. In Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning @ ECMLPKDD 2017, 49–58. 2017. URL: http://ceur-ws.org/Vol-1924/ialatecml_paper4.pdf.

[DCS10]

Pinar Donmez, Jaime Carbonell, and Jeff Schneider. A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy. In Proceedings of the 2010 SIAM International Conference on Data Mining, 826–837. 2010. URL: https://epubs.siam.org/doi/abs/10.1137/1.9781611972801.72.

[FZL+12]

M. Fang, X. Zhu, B. Li, W. Ding, and X. Wu. Self-taught active learning from crowds. In Proceedings of the 12th International Conference on Data Mining, 858–863. 2012. URL: https://ieeexplore.ieee.org/document/6413841/.

[PerezG07]

Fernando Pérez and Brian E. Granger. IPython: a system for interactive scientific computing. Computing in Science and Engineering, 9(3):21–29, 2007. URL: https://ipython.org.

[Set09]

Burr Settles. Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison, 2009. URL: http://burrsettles.com/pub/settles.activelearning.pdf.

[WCV11]

Stefan van der Walt, S. Chris Colbert, and Gael Varoquaux. The NumPy Array: A Structure for Efficient Numerical Computation. Computing in Science and Engineering, 13(2):22–30, 2011. URL: https://doi.org/10.1109/MCSE.2011.37.

[WWB+09]

Jacob Whitehill, Ting-fan Wu, Jacob Bergsma, Javier R. Movellan, and Paul L. Ruvolo. Whose vote should count more: optimal integration of labels from labelers of unknown expertise. In Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 2035–2043. Curran Associates, Inc., 2009. URL: http://papers.nips.cc/paper/3644-whose-vote-should-count-more-optimal-integration-of-labels-from-labelers-of-unknown-expertise.pdf.

[YLC+17]

Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, and Hsuan-Tien Lin. libact: Pool-based Active Learning in Python. Technical Report, National Taiwan University, 2017. available as arXiv preprint https://arxiv.org/abs/1710.00379. URL: https://github.com/ntucllab/libact.

[ZWS15]

J. Zhang, X. Wu, and V. S. Shengs. Active Learning With Imbalanced Multiple Noisy Labeling. IEEE Transactions on Cybernetics, 45(5):1095–1107, 2015. URL: https://ieeexplore.ieee.org/document/6878424/.

[ZTZ15]

Jinhong Zhong, Ke Tang, and Zhi-Hua Zhou. Active Learning from Crowds with Unsure Option. In Proceedings of the 24th International Joint Conference on Artificial Intelligence, 1061–1068. 2015. URL: https://www.ijcai.org/Proceedings/15/Papers/154.pdf.