Github user elfausto commented on the issue:
https://github.com/apache/spark/pull/23146
Thanks for your review, @srowen.
I added a link to the PR's description and the JIRA ticket pointing to the
reference Python implementation I used for unit testing.
Prior regularization fits nicely with taking a Bayesian approach to
Logistic Regression by posing a Gaussian prior and taking a Laplace
approximation to the posterior. I'm not sure about approaching other
regularized models in the same fashion.
As a reference, besides this being a textbook algorithm, the original
motivation for its implementation at [Affectv](https://affectv.com/), was the
following paper showing its application to display advertising: _Olivier
Chapelle , Eren Manavoglu , Romer Rosales, Simple and Scalable Response
Prediction for Display Advertising, ACM Transactions on Intelligent Systems and
Technology (TIST), v.5 n.4, January 2015_.
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