Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Decomposed Learning for Structured Prediction   Structured prediction is the cornerstone of several machine learning
applications. Unfortunately, in structured prediction settings with expressive
inter-variable interactions, exact inference-based learning algorithms, e.g.
Structural SVM, are often intractable. We present a new way, Decomposed
Learning (DecL), which performs efficient learning by restricting the inference
step to a limited part of the structured spaces. We provide characterizations
based on the structure, target parameters, and gold labels, under which DecL is
equivalent to exact learning. We then show that in real world settings, where
our theoretical assumptions may not completely hold, DecL-based algorithms are
significantly more efficient and as accurate as exact learning.

23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga