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Effective Meta-Regularization by Kernelized Proximal Regularization
Weisen Jiang · James Kwok · Yu Zhang

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @

We study the problem of meta-learning, which has proved to be advantageous to accelerate learning new tasks with a few samples. The recent approaches based on deep kernels achieve the state-of-the-art performance. However, the regularizers in their base learners are not learnable. In this paper, we propose an algorithm called MetaProx to learn a proximal regularizer for the base learner. We theoretically establish the convergence of MetaProx. Experimental results confirm the advantage of the proposed algorithm.

Author Information

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

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