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

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @
in Poster Session 5 »

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.

Keywords: Meta Learning

Author Information

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

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