Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mining Robust Default Configurations for Resource-constrained AutoML   Automatic machine learning (AutoML) is a key enabler of the mass deployment
of the next generation of machine learning systems. A key desideratum for
future ML systems is the automatic selection of models and hyperparameters. We
present a novel method of selecting performant configurations for a given task
by performing offline autoML and mining over a diverse set of tasks. By mining
the training tasks, we can select a compact portfolio of configurations that
perform well over a wide variety of tasks, as well as learn a strategy to
select portfolio configurations for yet-unseen tasks. The algorithm runs in a
zero-shot manner, that is without training any models online except the chosen
one. In a compute- or time-constrained setting, this virtually instant
selection is highly performant. Further, we show that our approach is effective
for warm-starting existing autoML platforms. In both settings, we demonstrate
an improvement on the state-of-the-art by testing over 62 classification and
regression datasets. We also demonstrate the utility of recommending
data-dependent default configurations that outperform widely used hand-crafted
defaults.

12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom