A Computability Perspective on (Verified) Machine Learning

Author:

Crook TonichaORCID,Morgan JayORCID,Pauly ArnoORCID,Roggenbach MarkusORCID

Publisher

Springer Nature Switzerland

Reference24 articles.

1. Ackerman, N., Asilis, J., Di, J., Freer, C., Tristan, J.B.: On the computable learning of continuous features. In: Presentation at CCA 2021 (2021)

2. de Brecht, M., Pauly, A.: Noetherian Quasi-Polish spaces. In: 26th EACSL Annual Conference on Computer Science Logic (CSL 2017). LIPIcs, vol. 82, pp. 16:1–16:17 (2017)

3. Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: IEEE Symposium on Security and Privacy, pp. 39–57 (2017)

4. D’Amour, A., et al.: Underspecification presents challenges for credibility in modern machine learning. J. Mach. Learn. Res. 23(226), 1–61 (2022). http://jmlr.org/papers/v23/20-1335.html

5. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

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