Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fundamental Limits of Online and Distributed Algorithms for Statistical
  Learning and Estimation   Many machine learning approaches are characterized by information constraints
on how they interact with the training data. These include memory and
sequential access constraints (e.g. fast first-order methods to solve
stochastic optimization problems); communication constraints (e.g. distributed
learning); partial access to the underlying data (e.g. missing features and
multi-armed bandits) and more. However, currently we have little understanding
how such information constraints fundamentally affect our performance,
independent of the learning problem semantics. For example, are there learning
problems where any algorithm which has small memory footprint (or can use any
bounded number of bits from each example, or has certain communication
constraints) will perform worse than what is possible without such constraints?
In this paper, we describe how a single set of results implies positive answers
to the above, for several different settings.

18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph