Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot
  Meta-Learning   Despite recent advances in its theoretical understanding, there still remains
a significant gap in the ability of existing PAC-Bayesian theories on
meta-learning to explain performance improvements in the few-shot learning
setting, where the number of training examples in the target tasks is severely
limited. This gap originates from an assumption in the existing theories which
supposes that the number of training examples in the observed tasks and the
number of training examples in the target tasks follow the same distribution,
an assumption that rarely holds in practice. By relaxing this assumption, we
develop two PAC-Bayesian bounds tailored for the few-shot learning setting and
show that two existing meta-learning algorithms (MAML and Reptile) can be
derived from our bounds, thereby bridging the gap between practice and
PAC-Bayesian theories. Furthermore, we derive a new computationally-efficient
PACMAML algorithm, and show it outperforms existing meta-learning algorithms on
several few-shot benchmark datasets.

6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
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The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-th