Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mutual information-based group explainers with coalition structure for
  machine learning model explanations   In this article, we propose and investigate ML group explainers in a general
game-theoretic setting with the focus on coalitional game values and games
based on the conditional and marginal expectation of an ML model. The
conditional game takes into account the joint distribution of the predictors,
while the marginal game depends on the structure of the model. The objective of
the article is to unify the two points of view under predictor dependencies and
to reduce the complexity of group explanations. To achieve this, we propose a
feature grouping technique that employs an information-theoretic measure of
dependence and design appropriate groups explainers. Furthermore, in the
context of coalitional game values with a two-step formulation, we introduce a
theoretical scheme that generates recursive coalitional game values under a
partition tree structure and investigate the properties of the corresponding
group explainers.

5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph