Perceptrons Under Verifiable Random Data Corruption

Author:

Escamilla Jose E. AguilarORCID,Diochnos Dimitrios I.ORCID

Publisher

Springer Nature Switzerland

Reference30 articles.

1. Barocas, S., Hardt, M., Narayanan, A.: Fairness and machine learning: limitations and opportunities. fairmlbook.org (2019). http://www.fairmlbook.org

2. Baum, E.: The perceptron algorithm is fast for non-malicious distributions. In: NeurIPS 1989, vol. 2, pp. 676–685. Morgan-Kaufmann (1989)

3. Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. In: ICML 2012. icml.cc/Omnipress (2012)

4. Brown, T.B., et al.: Language models are few-shot learners. In: NeurIPS 2020, Virtual (2020)

5. Quiñonero Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset Shift in Machine Learning. The MIT Press, Cambridge (2008)

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