Adaptive Smoothed Online Multi-Task Learning

Part of Advances in Neural Information Processing Systems 29 (NIPS 2016)

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Keerthiram Murugesan, Hanxiao Liu, Jaime Carbonell, Yiming Yang


This paper addresses the challenge of jointly learning both the per-task model parameters and the inter-task relationships in a multi-task online learning setting. The proposed algorithm features probabilistic interpretation, efficient updating rules and flexible modulation on whether learners focus on their specific task or on jointly address all tasks. The paper also proves a sub-linear regret bound as compared to the best linear predictor in hindsight. Experiments over three multi-task learning benchmark datasets show advantageous performance of the proposed approach over several state-of-the-art online multi-task learning baselines.