Learning to Teach Fairness-Aware Deep Multi-task Learning-Reference-Cited by-同舟云学术

Learning to Teach Fairness-Aware Deep Multi-task Learning

Published:2023 Issue: Volume: Page:710-726
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases
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Author:

Roy Arjun,Ntoutsi Eirini

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-26387-3_43

Reference24 articles.

1. Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: ICM, pp. 794–803. PMLR (2018)

2. Ding, F., Hardt, M., Miller, J., Schmidt, L.: Retiring adult: new datasets for fair machine learning. In: NeurIPS 34 (2021)

3. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);N Dong,2021

4. Donini, M., Oneto, L., Ben-David, S., Shawe-Taylor, J., Pontil, M.: Empirical risk minimization under fairness constraints. In: NeurIPS, pp. 2796–2806 (2018)

5. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: ITCS, pp. 214–226 (2012)

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