Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model-agnostic and Scalable Counterfactual Explanations via
  Reinforcement Learning   Counterfactual instances are a powerful tool to obtain valuable insights into
automated decision processes, describing the necessary minimal changes in the
input space to alter the prediction towards a desired target. Most previous
approaches require a separate, computationally expensive optimization procedure
per instance, making them impractical for both large amounts of data and
high-dimensional data. Moreover, these methods are often restricted to certain
subclasses of machine learning models (e.g. differentiable or tree-based
models). In this work, we propose a deep reinforcement learning approach that
transforms the optimization procedure into an end-to-end learnable process,
allowing us to generate batches of counterfactual instances in a single forward
pass. Our experiments on real-world data show that our method i) is
model-agnostic (does not assume differentiability), relying only on feedback
from model predictions; ii) allows for generating target-conditional
counterfactual instances; iii) allows for flexible feature range constraints
for numerical and categorical attributes, including the immutability of
protected features (e.g. gender, race); iv) is easily extended to other data
modalities such as images.

13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat