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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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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Detecting Adversarial Advertisements in the Wild
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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Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety
Suratna Budalakoti
Ashok N. Srivastava
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 39(2009), pp. 101-113
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We present a set of novel algorithms which we call sequence Miner that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise from recordings of switch sensors in the cockpits of commercial airliners. While the algorithms that we present are general and domain-independent, we focus on a specific problem that is critical to determining the system-wide health of a fleet of aircraft. The approach taken uses unsupervised clustering of sequences using the normalized length of the longest common subsequence as a similarity measure, followed by detailed outlier analysis to detect anomalies. In this method, an outlier sequence is defined as a sequence that is far away from the cluster center. We present new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence is deemed to be an outlier. The algorithms provide a coherent description to an analyst of the anomalies in the sequence when compared to more normal sequences. In the final section of the paper, we demonstrate the effectiveness of sequence Miner for anomaly detection on a real set of discrete-sequence data from a fleet of commercial airliners. We show that sequence Miner discovers actionable and operationally significant safety events. We also compare our innovations with standard Hidden Markov Models, and show that our methods are superior.
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