We study an extension of the "standard" learning models to settings where observing the value of an attribute has an associated cost (which might be different for differ...
We propose an In-Network Data-Centric Storage (INDCS) scheme for answering ad-hoc queries in sensor networks. Previously proposed In-Network Storage (INS) schemes suffered from St...
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reprodu...
Alexander J. Smola, Arthur Gretton, Le Song, Bernh...
Many applications of supervised learning require good generalization from limited labeled data. In the Bayesian setting, we can try to achieve this goal by using an informative pr...
We consider the problem of assigning prices to goods of fixed marginal cost in order to maximize revenue in the presence of single-minded customers. We focus in particular on the...