Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A SMART Stochastic Algorithm for Nonconvex Optimization with
  Applications to Robust Machine Learning   In this paper, we show how to transform any optimization problem that arises
from fitting a machine learning model into one that (1) detects and removes
contaminated data from the training set while (2) simultaneously fitting the
trimmed model on the uncontaminated data that remains. To solve the resulting
nonconvex optimization problem, we introduce a fast stochastic
proximal-gradient algorithm that incorporates prior knowledge through nonsmooth
regularization. For datasets of size $n$, our approach requires
$O(n^{2/3}/\varepsilon)$ gradient evaluations to reach $\varepsilon$-accuracy
and, when a certain error bound holds, the complexity improves to $O(\kappa
n^{2/3}\log(1/\varepsilon))$. These rates are $n^{1/3}$ times better than those
achieved by typical, full gradient methods.

4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg