tolochinsky & feldman
Reviews: On Coresets for Logistic Regression
The goal of this paper is to speed up logistic regression using a coreset based approach. The key idea is to "compress" the data set into a small fake set of points (called coreset) and to then train on that small set. The authors first show that, in general, no sublinear size coreset can exist. Then, they provide an algorithm that provides small summaries for certain data sets that satisfy a complexity assumption. Finally, they empirically compare that algorithm to two competing methods.