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Subspace Learning for Effective Meta-Learning
Weisen Jiang · James Kwok · Yu Zhang

Thu Jul 21 08:20 AM -- 08:25 AM (PDT) @ Room 309
in Miscellaneous Aspects of Machine Learning »

Meta-learning aims to extract meta-knowledge from historical tasks to accelerate learning on new tasks. Typical meta-learning algorithms like MAML learn a globally-shared meta-model for all tasks. However, when the task environments are complex, task model parameters are diverse and a common meta-model is insufficient to capture all the meta-knowledge. To address this challenge, in this paper, task model parameters are structured into multiple subspaces, and each subspace represents one type of meta-knowledge. We propose an algorithm to learn the meta-parameters (\ie, subspace bases). We theoretically study the generalization properties of the learned subspaces. Experiments on regression and classification meta-learning datasets verify the effectiveness of the proposed algorithm.

Keywords: OPT: Bilevel optimization · MISC: Transfer, Multitask and Meta-learning

Author Information

Weisen Jiang (Hong Kong University of Science and Technology)
James Kwok (Hong Kong University of Science and Technology)
Yu Zhang (Hong Kong University of Science and Technology)

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