Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Light Touch for Heavily Constrained SGD   Minimizing empirical risk subject to a set of constraints can be a useful
strategy for learning restricted classes of functions, such as monotonic
functions, submodular functions, classifiers that guarantee a certain class
label for some subset of examples, etc. However, these restrictions may result
in a very large number of constraints. Projected stochastic gradient descent
(SGD) is often the default choice for large-scale optimization in machine
learning, but requires a projection after each update. For heavily-constrained
objectives, we propose an efficient extension of SGD that stays close to the
feasible region while only applying constraints probabilistically at each
iteration. Theoretical analysis shows a compelling trade-off between
per-iteration work and the number of iterations needed on problems with a large
number of constraints.

26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th