Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-Asymptotic Analysis of Stochastic Approximation Algorithms for
  Streaming Data   Motivated by the high-frequency data streams continuously generated,
real-time learning is becoming increasingly important. These data streams
should be processed sequentially with the property that the stream may change
over time. In this streaming setting, we propose techniques for minimizing a
convex objective through unbiased estimates of its gradients, commonly referred
to as stochastic approximation problems. Our methods rely on stochastic
approximation algorithms due to their computationally advantage as they only
use the previous iterate as a parameter estimate. The reasoning includes
iterate averaging that guarantees optimal statistical efficiency under
classical conditions. Our non-asymptotic analysis shows accelerated convergence
by selecting the learning rate according to the expected data streams. We show
that the average estimate converges optimally and robustly to any data stream
rate. In addition, noise reduction can be achieved by processing the data in a
specific pattern, which is advantageous for large-scale machine learning. These
theoretical results are illustrated for various data streams, showing the
effectiveness of the proposed algorithms.

4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ