Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Optimization with Bandit Sampling   Many stochastic optimization algorithms work by estimating the gradient of
the cost function on the fly by sampling datapoints uniformly at random from a
training set. However, the estimator might have a large variance, which
inadvertently slows down the convergence rate of the algorithms. One way to
reduce this variance is to sample the datapoints from a carefully selected
non-uniform distribution. In this work, we propose a novel non-uniform sampling
approach that uses the multi-armed bandit framework. Theoretically, we show
that our algorithm asymptotically approximates the optimal variance within a
factor of 3. Empirically, we show that using this datapoint-selection technique
results in a significant reduction in the convergence time and variance of
several stochastic optimization algorithms such as SGD, SVRG and SAGA. This
approach for sampling datapoints is general, and can be used in conjunction
with any algorithm that uses an unbiased gradient estimation -- we expect it to
have broad applicability beyond the specific examples explored in this work.

12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int