Proving Data-Poisoning Robustness in Decision Trees

2 Dec 2019Samuel DrewsAws AlbarghouthiLoris D'Antoni

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... (read more)

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