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Proving Data-Poisoning Robustness in Decision Trees
[article]
2020
arXiv
pre-print
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 model. We target decision-tree models, a popular and simple class of machine learning models that underlies many complex learning techniques. We present a sound verification technique based on
arXiv:1912.00981v2
fatcat:tk6ep2amnjhgljbr3ngmewdwpy