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Adaptive Adversarial Multi-task Representation Learning
YUREN MAO · Weiwei Liu · Xuemin Lin

Thu Jul 16 06:00 AM -- 06:45 AM & Thu Jul 16 06:00 PM -- 06:45 PM (PDT) @ Virtual

Adversarial Multi-task Representation Learning (AMTRL) methods are able to boost the performance of Multi-task Representation Learning (MTRL) models. However, the theoretical mechanism behind AMTRL is less investigated. To fill this gap, we study the generalization error bound of AMTRL through the lens of Lagrangian duality . Based on the duality, we proposed an novel adaptive AMTRL algorithm which improves the performance of original AMTRL methods. The extensive experiments back up our theoretical analysis and validate the superiority of our proposed algorithm.

Author Information

YUREN MAO (School of Computer Science and Engineering, University of New South Wales)
Weiwei Liu (Wuhan University)
Xuemin Lin (University of New South Wales)

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