Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Augmentation Distributions using Transformed Risk Minimization   Adapting to the structure of data distributions (such as symmetry and
transformation invariances) is an important challenge in machine learning.
Invariances can be built into the learning process by architecture design, or
by augmenting the dataset. Both require a priori knowledge about the exact
nature of the symmetries. Absent this knowledge, practitioners resort to
expensive and time-consuming tuning. To address this problem, we propose a new
approach to learn distributions of augmentation transforms, in a new
\emph{Transformed Risk Minimization} (TRM) framework. In addition to predictive
models, we also optimize over transformations chosen from a hypothesis space.
As an algorithmic framework, our TRM method is (1) efficient (jointly learns
augmentations and models in a \emph{single training loop}), (2) modular (works
with \emph{any} training algorithm), and (3) general (handles \emph{both
discrete and continuous} augmentations). We theoretically compare TRM with
standard risk minimization, and give a PAC-Bayes upper bound on its
generalization error. We propose to optimize this bound over a rich
augmentation space via a new parametrization over compositions of blocks,
leading to the new \emph{Stochastic Compositional Augmentation Learning}
(SCALE) algorithm. We compare SCALE experimentally with prior methods (Fast
AutoAugment and Augerino) on CIFAR10/100, SVHN . Additionally, we show that
SCALE can correctly learn certain symmetries in the data distribution
(recovering rotations on rotated MNIST) and can also improve calibration of the
learned model.

22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas