Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Centroid Approximation for Bootstrap   Bootstrap is a principled and powerful frequentist statistical tool for
uncertainty quantification. Unfortunately, standard bootstrap methods are
computationally intensive due to the need of drawing a large i.i.d. bootstrap
sample to approximate the ideal bootstrap distribution; this largely hinders
their application in large-scale machine learning, especially deep learning
problems. In this work, we propose an efficient method to explicitly
\emph{optimize} a small set of high quality "centroid" points to better
approximate the ideal bootstrap distribution. We achieve this by minimizing a
simple objective function that is asymptotically equivalent to the Wasserstein
distance to the ideal bootstrap distribution. This allows us to provide an
accurate estimation of uncertainty with a small number of bootstrap centroids,
outperforming the naive i.i.d. sampling approach. Empirically, we show that our
method can boost the performance of bootstrap in a variety of applications.

29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex