References

Below, we provide a short bibliography on the topics of the workshop.

Active Learning

[1] B. Settles. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison, 2009.

[2] D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine learning , 15(2):201–221, 1994.

[3] A. Beygelzimer, S. Dasgupta and J. Langford Importance Weighted Active Learning. ICML, 2009.

[4] S. Dasgupta, D. Hsu, and C. Monteleoni. A general agnostic active learning algorithm. NIPS 2007.

[5] M. Balcan , A. Broder, and T. Zhang. Margin Based Active Learning. COLT, 2007.

[6] K. Chaudhuri and C. Zhang. Beyond disagreement-based agnostic active learning. NIPS, 2014.

[7] R. El-Yaniv and Y. Wiener. Active learning via perfect selective classification. JMLR, 2012 .

Crowdsourcing

[8] Vaughan, Jennifer Wortman. Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research. JMLR, 2017.

[9] Kittur, Aniket, Ed H. Chi, and Bongwon Suh. Crowdsourcing user studies with Mechanical Turk. CHI 2008.

[10] Raykar, V. C., S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy. Learning from crowds. JMLR 2010.

[11] Donmez, Pinar, Jaime G. Carbonell, and Jeff Schneider. Efficiently learning the accuracy of labeling sources for selective sampling. KDD 2009.

[12] O. Dekel and O. Shamir. Vox Populi: Collecting high-quality labels from a crowd. COLT 2009.

Fairness in Machine Learning

[13] Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, and Hanna Wallach. Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? CHI 2019.

[14] D. Pedreschi, S. Ruggieri, and F. Turini. Discrimination-aware data mining. KDD 2008.

[15] Zemel, Rich, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. Learning fair representations. ICML 2013.

[16] Kamishima, Toshihiro, Shotaro Akaho, and Jun Sakuma. Fairness-aware learning through regularization approach. ICDM Workshops 2011.

[17] Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard S. Zemel. Fairness through awareness. ITCS 2012.

[18] Luong, Binh Thanh, Salvatore Ruggieri, and Franco Turini. k-NN as an implementation of situation testing for discrimination discovery and prevention. KDD 2011.

Others

[19] Zhu, Xiaojin. Semi-supervised learning literature survey. Computer Science, University of Wisconsin-Madison, 2006.

[20] Kaelbling, Leslie Pack, Michael L. Littman, and Andrew W. Moore. Reinforcement learning: A survey. Journal of artificial intelligence research, 1996.