This paper investigates a class of learning problems called learning satisfiability (LSAT) problems, where the goal is to learn a set in the input (feature) space that satisfies...
Frederic Thouin, Mark Coates, Brian Eriksson, Robe...
Estimates of predicate selectivities by database query optimizers often differ significantly from those actually encountered during query execution, leading to poor plan choices a...
We describe work on automatically assigning labels to books using user-defined tags as the label set. Using supervised learning and exploring both binary and multiclass classifica...
Modeling subspaces of a distribution of interest in high dimensional spaces is a challenging problem in pattern analysis. In this paper, we present a novel framework for pose inva...
In order to enable scalable querying of graph databases, intelligent selection of subgraphs to index is essential. An improved index can reduce response times for graph queries si...