Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Adding the trusting classifiers paper from the Seattle papers we love #466

Merged
merged 1 commit into from Jul 9, 2017
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
2 changes: 2 additions & 0 deletions machine_learning/README.md
Expand Up @@ -15,6 +15,8 @@

* [Applications of Machine Learning to Location Data](http://www.berkkapicioglu.com/wp-content/uploads/2013/11/thesis_final.pdf) - Using machine learning to design and analyze novel algorithms that leverage location data.

* ["Why Should I Trust You?" Explaining the Predictions of Any Classifier](http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf) - This paper introduces an explanation technique for any classifier in a interpretable manner.

## Hosted Papers

* :scroll: **[A Sparse Johnson-Lindenstrauss Transform](dimensionality_reduction/a-sparse-johnson-lindenstrauss-transform.pdf)**
Expand Down