Multi-task learning on the edge: cost-efficiency and theoretical optimality
arXiv:2110.04639
Abstract
This article proposes a distributed multi-task learning (MTL) algorithm based on supervised principal component analysis (SPCA) which is: (i) theoretically optimal for Gaussian mixtures, (ii) computationally cheap and scalable. Supporting experiments on synthetic and real benchmark data demonstrate that significant energy gains can be obtained with no performance loss.
arXiv comment4 pages, 5 figures, code to reproduce figure available at: https://github.com/Sami-fak/DistributedMTLSPCA