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2020 ICML ICML 2020

Adaptive Adversarial Multi-task Representation Learning

Abstract

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🧭 Keyword Pioneer — multi-task representation learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

Authors

Yuren Mao , Weiwei Liu , Xuemin Lin

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Representation Learning Machine Learning > Learning Types > Adversarial Learning Machine Learning > Learning Types > Multi-Task Learning Machine Learning > Learning Types > Transfer Learning Machine Learning > Learning Paradigms > Multi-Task Learning Deep Learning > Learning Types > Multi-Modal Learning

Keywords

representation learning adversarial learning multi-task learning transfer learning gradient-based optimization generalization error bound generalization bound multi-task representation learning adversarial multi-task lagrangian duality reward transformation
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