Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Achievability results for statistical learning under communication
  constraints   The problem of statistical learning is to construct an accurate predictor of
a random variable as a function of a correlated random variable on the basis of
an i.i.d. training sample from their joint distribution. Allowable predictors
are constrained to lie in some specified class, and the goal is to approach
asymptotically the performance of the best predictor in the class. We consider
two settings in which the learning agent only has access to rate-limited
descriptions of the training data, and present information-theoretic bounds on
the predictor performance achievable in the presence of these communication
constraints. Our proofs do not assume any separation structure between
compression and learning and rely on a new class of operational criteria
specifically tailored to joint design of encoders and learning algorithms in
rate-constrained settings.

15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat