Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nothing Else Matters: Model-Agnostic Explanations By Identifying
  Prediction Invariance   At the core of interpretable machine learning is the question of whether
humans are able to make accurate predictions about a model's behavior. Assumed
in this question are three properties of the interpretable output: coverage,
precision, and effort. Coverage refers to how often humans think they can
predict the model's behavior, precision to how accurate humans are in those
predictions, and effort is either the up-front effort required in interpreting
the model, or the effort required to make predictions about a model's behavior.
  In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that
produces high-precision rule-based explanations for which the coverage
boundaries are very clear. We compare aLIME to linear LIME with simulated
experiments, and demonstrate the flexibility of aLIME with qualitative examples
from a variety of domains and tasks.

17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: eess
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga