Topics in matrix analysis
Mathematical Programming: Series A and B
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
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
Convex Optimization
Combining winnow and orthogonal sparse bigrams for incremental spam filtering
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
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
Learning to classify with missing and corrupted features
Machine Learning
Stackelberg games for adversarial prediction problems
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Approaches to adversarial drift
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
Is data clustering in adversarial settings secure?
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
| Hi-index | 0.00 |
The standard assumption of identically distributed training and test data is violated when the test data are generated in response to the presence of a predictive model. This becomes apparent, for example, in the context of email spam filtering. Here, email service providers employ spam filters, and spam senders engineer campaign templates to achieve a high rate of successful deliveries despite the filters. We model the interaction between the learner and the data generator as a static game in which the cost functions of the learner and the data generator are not necessarily antagonistic. We identify conditions under which this prediction game has a unique Nash equilibrium and derive algorithms that find the equilibrial prediction model. We derive two instances, the Nash logistic regression and the Nash support vector machine, and empirically explore their properties in a case study on email spam filtering.