06/12/2021

Effective Meta-Regularization by Kernelized Proximal Regularization

Weisen Jiang, James Kwok, Yu Zhang

Keywords: meta learning

Abstract Paper Similar Papers

Abstract: 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.

 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at NeurIPS 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 5:09 
 0
 0
 0
 0
 13:22 
 0
 0
 0
 0
 3:54 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 2:59 
 0
 0
 0
 0
 2:38 
 0
 0
 0
 0
 15:03 
 0
 0
 0
 0
 13:08 
 0
 0
 0
 0
 5:22 
 0
 0
 0
 0
 11:27 
 0
 0
 0
 0
 13:43 
 0
 0
 0
 0
 5:18 
 0
 0
 0
 0
 4:46 
 0
 0
 0
 0
 3:11 
 0
 0
 0
 0
 14:50 
 0
 0
 0
 0
 6:14 
 0
 0
 0
 0
 16:57 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 12:04 
 0
 0
 0
 0
 4:15 
 1
 1
 0
 0
 18:26 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 7:26 
 0
 0
 0
 0
 12:03 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 13:54 
 0
 0
 0
 0
 16:00 
 0
 0
 0
 0
 5:25 
 0
 0
 0
 0
 15:13 
 0
 0
 0
 0
 5:13 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 12:01 
 0
 0
 0
 0
 6:02 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 5:26 
 0
 0
 0
 0
 15:04 
 0
 0
 0
 0
 8:27 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 4:59