A Theory of Learning with Competing Objectives and User Feedback

Author:

Awasthi Pranjal,Cortes Corinna,Mansour Yishay,Mohri Mehryar

Publisher

Springer Nature Switzerland

Reference49 articles.

1. Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., Wallach, H.: A reductions approach to fair classification. arXiv preprint arXiv:1803.02453 (2018)

2. Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias: there’s software used across the country to predict future criminals. and it’s biased against blacks 2016 (2019). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

3. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002)

4. Bastani, O., Zhang, X., Solar-Lezama, A.: Probabilistic verification of fairness properties via concentration. Proc. ACM Program. Lang. 3(OOPSLA), 1–27 (2019)

5. Bechavod, Y., Jung, C., Wu, Z.S.: Metric-free individual fairness in online learning. arXiv preprint arXiv:2002.05474 (2020)

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