Case-Specific Random Forests

By Ruo Xu, Dan Nettleton, Daniel J. Nordman. 2016

37 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Stefan Wager
2. Susan Athey
3. Julie Tibshirani
4. Rina Friedberg
5. Guido W. Imbens
6. Mihaela van der Schaar
7. Ahmed M. Alaa
8. Nathan Kallus

In Genetics

1. Rina Friedberg
2. Julie Tibshirani
3. Susan Athey
4. Stefan Wager

Citations in the corpus

• Local Linear Forests (2018, background): “Case-specific random forests, introduced by Xu, Nettleton, and Nordman (2016), use local information to upweight training samples not at the prediction step, but during the bootstrap to generate datasets for each tree.”