JMLR and arXiv submission,

nspyrison · May 11, 2022 · 2f1296e · 2f1296e
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@@ -16,7 +16,7 @@ author:
 #   address: Monash University\newline ORCiD 0000-0002-9813-0377
 #   email: [email protected]
 year: 2022
-date_submitted: "MM/DD"
+date_submitted: "05/11"
 abstract: "The increased predictive power of nonlinear models comes at the cost of interpretability of its terms. This trade-off has led to the emergence of eXplainable AI (XAI). XAI attempts to shed light on how models use predictors to arrive at a prediction with _local explanations_, a point estimate of the linear feature importance in the vicinity of one instance. These can be considered linear projections and can be further explored to understand better the interactions between features used to make predictions across the predictive model surface. Here we describe interactive linear interpolation used for exploration at any instance and illustrate with examples with categorical (penguin species, chocolate types) and quantitative (soccer/football salaries, house prices) output. The methods are implemented in the __R__ package __cheem__, available on CRAN."
 keywords: "explainable artificial intelligence, nonlinear model interpretability, visual analytics, local explanations, grand tour, radial tour"
 bibliography: cheem_paper.bib

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 % Heading arguments are {volume}{year}{pages}{date submitted}{date published}{paper id}{author-full-names}
 
-\jmlrheading{}{2022}{}{MM/DD}{--}{}{%
+\jmlrheading{}{2022}{}{05/11}{--}{}{%
 Nicholas Spyrison and Dianne Cook}
 
 % Short headings should be running head and authors last names

diff --git a/jmlr_coverletter.docx b/jmlr_coverletter.docx
diff --git a/jmlr_coverletter.pdf b/jmlr_coverletter.pdf