A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
A robust minimax approach to classification
The Journal of Machine Learning Research
Mathematical Programming: Series A and B
Nightmare at test time: robust learning by feature deletion
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning to classify with missing and corrupted features
Proceedings of the 25th international conference on Machine learning
A Game Theoretical Model for Adversarial Learning
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Learning to classify with missing and corrupted features
Machine Learning
Classifier evaluation and attribute selection against active adversaries
Data Mining and Knowledge Discovery
Adversarial support vector machine learning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Protecting location privacy: optimal strategy against localization attacks
Proceedings of the 2012 ACM conference on Computer and communications security
An efficient adversarial learning strategy for constructing robust classification boundaries
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Static prediction games for adversarial learning problems
The Journal of Machine Learning Research
Approaches to adversarial drift
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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The standard assumption of identically distributed training and test data is violated when test data are generated in response to a predictive model. This becomes apparent, for example, in the context of email spam filtering, where an email service provider employs a spam filter and the spam sender can take this filter into account when generating new emails. We model the interaction between learner and data generator as a Stackelberg competition in which the learner plays the role of the leader and the data generator may react on the leader's move. We derive an optimization problem to determine the solution of this game and present several instances of the Stackelberg prediction game. We show that the Stackelberg prediction game generalizes existing prediction models. Finally, we explore properties of the discussed models empirically in the context of email spam filtering.