In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are ...
We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a repr...
Abstract. While the tightest proven worst-case complexity for Andersen's points-to analysis is nearly cubic, the analysis seems to scale better on real-world codes. We examine...
As document collections grow larger, the information needs and relevance judgments in a test collection must be well-chosen within a limited budget to give the most reliable and ro...
Ben Carterette, Virgiliu Pavlu, Evangelos Kanoulas...
We consider the problem of online learning in a changing environment under sparse user feedback. Specifically, we address the classification of music types according to a user...