Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ensembling Shift Detectors: an Extensive Empirical Evaluation   The term dataset shift refers to the situation where the data used to train a
machine learning model is different from where the model operates. While
several types of shifts naturally occur, existing shift detectors are usually
designed to address only a specific type of shift. We propose a simple yet
powerful technique to ensemble complementary shift detectors, while tuning the
significance level of each detector's statistical test to the dataset. This
enables a more robust shift detection, capable of addressing all different
types of shift, which is essential in real-life settings where the precise
shift type is often unknown. This approach is validated by a large-scale
statistically sound benchmark study over various synthetic shifts applied to
real-world structured datasets.

32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex