Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Shapley Values and Variational Autoencoders to Explain Predictive
  Models with Dependent Mixed Features   Shapley values are today extensively used as a model-agnostic explanation
framework to explain complex predictive machine learning models. Shapley values
have desirable theoretical properties and a sound mathematical foundation in
the field of cooperative game theory. Precise Shapley value estimates for
dependent data rely on accurate modeling of the dependencies between all
feature combinations. In this paper, we use a variational autoencoder with
arbitrary conditioning (VAEAC) to model all feature dependencies
simultaneously. We demonstrate through comprehensive simulation studies that
our VAEAC approach to Shapley value estimation outperforms the state-of-the-art
methods for a wide range of settings for both continuous and mixed dependent
features. For high-dimensional settings, our VAEAC approach with a non-uniform
masking scheme significantly outperforms competing methods. Finally, we apply
our VAEAC approach to estimate Shapley value explanations for the Abalone data
set from the UCI Machine Learning Repository.

23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
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The categories of this paper are: econ
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph