Adversarial Active Learning in the Presence of Weak and Malicious Oracles

Abstract

We present a robust active learning technique for situations where there are weak and adversarial oracles. Our work falls under the general umbrella of active learning in which training data is insufficient and oracles are queried to supply labels for the most informative samples to expand the training set. On top of that, we consider problems where a large percentage of oracles may be strategically lying, as in adversarial settings. We present an adversarial active learning technique that explores the duality between oracle modeling and data modeling. We demonstrate on real datasets that our adversarial active learning technique is superior to not only the heuristic majority-voting technique but one of the state-of-the-art adversarial crowdsourcing technique—Generative model of Labels, Abilities, and Difficulties (GLAD), when genuine oracles are outnumbered by weak oracles and malicious oracles, and even in the extreme cases where all the oracles are either weak or malicious. To put our technique under more rigorous tests, we compare our adversarial active learner to the ideal active learner that always receives correct labels. We demonstrate that our technique is as effective as the ideal active learner when only one third of the oracles are genuine.