# Out-of-Distribution Taxonomy (preview)

## A preview of our upcoming guide of fixing out-of-distribution errors in your machine learning models

Co-Authored by Vinay Prabhu

Below is a high-level preview of our high-level taxonomy of Out-of-Distribution (OOD, sometimes referred to as ODD for “Out-of-distribution-detection” as well) errors in machine learning, as it was presented at the recent Harvard Machine Learning Theory Virtual Meetup on October 30th, 2020.

Unlike other papers on the subject, our guide will provide actionable recommnendations for how to fix OOD error $x$, detected with diagnostic method $y$, by doing $z$. For example, consider the following popular OOD-detection methods (described with little context by Ovadia, Yaniv, et al.):

There is a time and place for all of these, but details on those times and places are hard to find (if they exist at all). Our aim is to provide a concrete guide on when it’s necessary to make the leap to bayesian machine learning, and when you can fix your problems with softmax tuning.

Cited as:

@article{Out-of-Distribution Taxonomy,
title = "Out-of-Distribution Taxonomy (preview)",
author = "Matthew McAteer and Vinay Prabhu",
journal = "matthewmcateer.me",
year = "2020",
url = "https://matthewmcateer.me/posts/ood-taxonomy/"
}

If you notice mistakes and errors in this post, don’t hesitate to contact me at [contact at matthewmcateer dot me] and I would be very happy to correct them right away!

See you in the next post 😄

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