Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Meta Learning on a Sequence of Imbalanced Domains with Difficulty
  Awareness   Recognizing new objects by learning from a few labeled examples in an
evolving environment is crucial to obtain excellent generalization ability for
real-world machine learning systems. A typical setting across current meta
learning algorithms assumes a stationary task distribution during meta
training. In this paper, we explore a more practical and challenging setting
where task distribution changes over time with domain shift. Particularly, we
consider realistic scenarios where task distribution is highly imbalanced with
domain labels unavailable in nature. We propose a kernel-based method for
domain change detection and a difficulty-aware memory management mechanism that
jointly considers the imbalanced domain size and domain importance to learn
across domains continuously. Furthermore, we introduce an efficient adaptive
task sampling method during meta training, which significantly reduces task
gradient variance with theoretical guarantees. Finally, we propose a
challenging benchmark with imbalanced domain sequences and varied domain
difficulty. We have performed extensive evaluations on the proposed benchmark,
demonstrating the effectiveness of our method. We made our code publicly
available.

25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th