TY  - JOUR
A1  - Brückner, Michael
A1  - Kanzow, Christian
A1  - Scheffer, Tobias
T1  - Static prediction games for adversarial learning problems
JF  - Journal of machine learning research
N2  - 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.
KW  - static prediction games
KW  - adversarial classification
KW  - Nash equilibrium
Y1  - 2012
SN  - 1532-4435
VL  - 13
SP  - 2617
EP  - 2654
PB  - Microtome Publishing
CY  - Cambridge, Mass.
ER  - 
TY  - JOUR
A1  - Bickel, Steffen
A1  - Brückner, Michael
A1  - Scheffer, Tobias
T1  - Discriminative learning under covariate shift
N2  - We address classification problems for which the training instances are governed by an input distribution that is allowed to differ arbitrarily from the test distribution-problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. The problem of learning under covariate shift can be written as an integrated optimization problem. Instantiating the general optimization problem leads to a kernel logistic regression and an exponential model classifier for covariate shift. The optimization problem is convex under certain conditions; our findings also clarify the relationship to the known kernel mean matching procedure. We report on experiments on problems of spam filtering, text classification, and landmine detection.
Y1  - 2009
UR  - http://jmlr.csail.mit.edu/
SN  - 1532-4435
ER  -