A coreset (or, core-set) for a given problem is a "compressed" representation of its input, in the sense that a solution for the problem with the (small) coreset as input would yield a provable (1+epsilon) approximation to the problem with the original (large) input.
Using traditional techniques, a coreset usually implies provable linear time algorithms for the corresponding optimization problem, which can be computed in parallel, via one pass over the data on the cloud, and using only logarithmic space (i.e, in the streaming model). During the recent years, coresets were designed for many problems in deep/machine learning, statistics, facility location, real-time systems, computer vision and robotics.
In this talk I will forge links between coresets for machine learning of streaming "Big Data" on the cloud, computational geometry, and robotic. In particular, coresets for matrix approximation, epsilon-nets, and autonomous toy-quadcopters.
Joint work with Soliman Nasser, Ibrahim Jubran and many more.
