Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient computation of contrastive explanations   With the increasing deployment of machine learning systems in practice,
transparency and explainability have become serious issues. Contrastive
explanations are considered to be useful and intuitive, in particular when it
comes to explaining decisions to lay people, since they mimic the way in which
humans explain. Yet, so far, comparably little research has addressed
computationally feasible technologies, which allow guarantees on uniqueness and
optimality of the explanation and which enable an easy incorporation of
additional constraints. Here, we will focus on specific types of models rather
than black-box technologies. We study the relation of contrastive and
counterfactual explanations and propose mathematical formalizations as well as
a 2-phase algorithm for efficiently computing (plausible) pertinent positives
of many standard machine learning models.

32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an