Adaptive Adversarial Multi-task Representation Learning

Adversarial Multi-task Representation Learning (AMTRL) methods are capable of boosting the performance of Multi-task Representation Learning (MTRL) models. However, the theoretical mechanism behind AMTRL has been only minimally investigated. Accordingly, to fill this gap, we study the generalization error bound of AMTRL through the lens of Lagrangian duality. Based on this duality, we propose a novel adaptive AMTRL algorithm that improves the performance of the original AMTRL methods. We further conduct extensive experiments to back up our theoretical analysis and validate the superiority of our proposed algorithm.