Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Q-FIT: The Quantifiable Feature Importance Technique for Explainable
  Machine Learning   We introduce a novel framework to quantify the importance of each input
feature for model explainability. A user of our framework can choose between
two modes: (a) global explanation: providing feature importance globally across
all the data points; and (b) local explanation: providing feature importance
locally for each individual data point. The core idea of our method comes from
utilizing the Dirichlet distribution to define a distribution over the
importance of input features. This particular distribution is useful in ranking
the importance of the input features as a sample from this distribution is a
probability vector (i.e., the vector components sum to 1), Thus, the ranking
uncovered by our framework which provides a \textit{quantifiable explanation}
of how significant each input feature is to a model's output. This quantifiable
explainability differentiates our method from existing feature-selection
methods, which simply determine whether a feature is relevant or not.
Furthermore, a distribution over the explanation allows to define a closed-form
divergence to measure the similarity between learned feature importance under
different models. We use this divergence to study how the feature importance
trade-offs with essential notions in modern machine learning, such as privacy
and fairness. We show the effectiveness of our method on a variety of synthetic
and real datasets, taking into account both tabular and image datasets.

12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org