Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MixRL: Data Mixing Augmentation for Regression using Reinforcement
  Learning   Data augmentation is becoming essential for improving regression accuracy in
critical applications including manufacturing and finance. Existing techniques
for data augmentation largely focus on classification tasks and do not readily
apply to regression tasks. In particular, the recent Mixup techniques for
classification rely on the key assumption that linearity holds among training
examples, which is reasonable if the label space is discrete, but has
limitations when the label space is continuous as in regression. We show that
mixing examples that either have a large data or label distance may have an
increasingly-negative effect on model performance. Hence, we use the stricter
assumption that linearity only holds within certain data or label distances for
regression where the degree may vary by each example. We then propose MixRL, a
data augmentation meta learning framework for regression that learns for each
example how many nearest neighbors it should be mixed with for the best model
performance using a small validation set. MixRL achieves these objectives using
Monte Carlo policy gradient reinforcement learning. Our experiments conducted
both on synthetic and real datasets show that MixRL significantly outperforms
state-of-the-art data augmentation baselines. MixRL can also be integrated with
other classification Mixup techniques for better results.

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The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc