“We investigate the computational efficiency of multitask learning of Boolean functions over the 𝑑-dimensional hypercube, that are related by means of a feature representation of size 𝑘≪𝑑 shared across all tasks. We present a polynomial time multitask learning algorithm for the concept class of halfspaces with margin 𝛾, which is based on a simultaneous boosting technique and requires only poly(𝑘/𝛾) samples-per-task and poly(𝑘log(𝑑)/𝛾) samples in total.”

Find the paper and full list of authors in the Machine Learning Research proceedings.