AIC Seminar Series

Many real-world domains, such as web spam, auction fraud, and counter-terrorism, are both relational and adversarial. Previous work in adversarial machine learning has assumed that instances are independent from each other, both when manipulated by an adversary and labeled by a classifier. Relational domains violate this assumption, since object labels depend on the labels of related objects as well as their own attributes.

In this talk, I will present a novel method for robustly performing collective classification in the presence of a malicious adversary that can modify up to a fixed number of binary-valued attributes. This method is formulated as a convex quadratic program that guarantees optimal weights against a worst-case adversary in polynomial time. In addition to increased robustness against active adversaries, this kind of adversarial regularization can also lead to improved generalization even when no adversary is present. In experiments on real and simulated data, our method consistently outperforms both non-adversarial and non-relational baselines.

Joint work with Ali Torkamani.

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