Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection,
  and Extreme Bandits   We present a novel distribution-free approach, the data-driven threshold
machine (DTM), for a fundamental problem at the core of many learning tasks:
choose a threshold for a given pre-specified level that bounds the tail
probability of the maximum of a (possibly dependent but stationary) random
sequence. We do not assume data distribution, but rather relying on the
asymptotic distribution of extremal values, and reduce the problem to estimate
three parameters of the extreme value distributions and the extremal index. We
specially take care of data dependence via estimating extremal index since in
many settings, such as scan statistics, change-point detection, and extreme
bandits, where dependence in the sequence of statistics can be significant. Key
features of our DTM also include robustness and the computational efficiency,
and it only requires one sample path to form a reliable estimate of the
threshold, in contrast to the Monte Carlo sampling approach which requires
drawing a large number of sample paths. We demonstrate the good performance of
DTM via numerical examples in various dependent settings.

37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg