This paper presents a detailed discussion of problem formulation and data representation issues in the design, deployment, and operation of a massive-scale machine learning system for targeted display advertising. Notably, the machine learning system itself is deployed and has been in continual...

We present a framework for active inference, the selective acquisition of labels for cases at prediction time in lieu of using the estimated labels of a predictive model. The framework generalizes prior work on prediction time label acquisition. We develop techniques within this active inference...

For many supervised learning tasks, the cost of acquiringtraining data is dominated by the cost of class labeling.In this work, we explore active learning forclass probability estimation (CPE). Active learning acquiresdata incrementally, using the model learned sofar to help identify especially...

This paper addresses focused information acquisition for predictive data mining. Asbusinesses strive to cater to the preferences of individual consumers, they often employpredictive models to customize marketing efforts. Building accurate models requiresinformation about consumer preferences...