Matthew Otey
Matthew Eric Otey received his B.S. degree from the University of Virginia in 2001 and his Ph.D. degree from The Ohio State University in 2006, both in computer science.
From 2006 to 2007 he worked for Mission Critical Technologies, Inc. at the NASA Ames Research Center where he worked on anomaly detection for aviation safety applications. He is currently a software engineer at Google, Inc. in Pittsburgh PA, where he is working on using machine learning to improve ads quality by detecting bad and dangerous ads and advertisers.
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Detecting Adversarial Advertisements in the Wild
D. Sculley
Michael Pohl
Bridget Spitznagel
John Hainsworth
Yunkai Zhou
Proceedings of the 17th ACM SIGKDD International Conference on Data Mining and Knowledge Discovery, KDD (2011)
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In a large online advertising system, adversaries may attempt to profit from the creation of low quality or harmful
advertisements. In this paper, we present a large scale data
mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. Because
both false positives and false negatives have high cost, our
deployed system uses a tiered strategy combining automated
and semi-automated methods to ensure reliable classification. We also employ strategies to address the challenges of
learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge,
and independently assessing the effectiveness of our system.
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Preview abstract
We study a novel variant of the domain adaptation problem, in which the loss function on test data changes due to dependencies on prior predictions. One important instance of this problem area occurs in settings where it is more costly to make a new error than to repeat a previous error. We propose several methods for learning effectively in this setting, and test them empirically on the real-world tasks of malicious URL classification and adversarial advertisement detection.
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