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Proving Data-Poisoning Robustness in Decision Trees.

Machine learning models are brittle, and small changes in the training data can result in different predictions. We study the problem of proving that a prediction is robust to data poisoning, where an attacker can inject a number of malicious elements into the training set to influence the learned m...

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Publicado en:Communications of the ACM Vol. 66; no. 2; pp. 105 - 114
Autores principales: Drews, Samuel, Albarghouthi, Aws, D'Antoni, Loris
Formato: Artículo
Publicado: Association for Computing Machinery Feb2023
Materias:
Machine learning
Decision trees
Data integrity
Computer hacking
Robust control
Acceso en línea:Ver este registro en EBSCOhost