Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning
Paper summary Cao et al. propose KARMA, a method to defend against data poisening in an online learning system where training examples are obtained through crowdsourcing. The setting, however, is somewhat constrained and can be described as human-in-the-loop. In particular, there is the system, which is maintained by an administrator, and there are users – among them there might be users with malicious intents, i.e. attackers. KARMA consists of two steps: identifying (possibly polluted) training examples that cause mis-classification of samples within a small oracle set; and then correcting these problems by removing clusters of polluted samples. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).



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