Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Model Selection for Prediction Under a Budget   We present a dynamic model selection approach for resource-constrained
prediction. Given an input instance at test-time, a gating function identifies
a prediction model for the input among a collection of models. Our objective is
to minimize overall average cost without sacrificing accuracy. We learn gating
and prediction models on fully labeled training data by means of a bottom-up
strategy. Our novel bottom-up method is a recursive scheme whereby a
high-accuracy complex model is first trained. Then a low-complexity gating and
prediction model are subsequently learnt to adaptively approximate the
high-accuracy model in regions where low-cost models are capable of making
highly accurate predictions. We pose an empirical loss minimization problem
with cost constraints to jointly train gating and prediction models. On a
number of benchmark datasets our method outperforms state-of-the-art achieving
higher accuracy for the same cost.

13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con