2 papers
cs.LG2021
Feature Importance Explanations for Temporal Black-Box Models
Akshay Sood, Mark Craven
Models in the supervised learning framework may capture rich and complex representations over the features that are hard for humans to interpret. Existing methods to explain such m…
cs.LG2018
Understanding Learned Models by Identifying Important Features at the Right Resolution
Kyubin Lee, Akshay Sood, Mark Craven
In many application domains, it is important to characterize how complex learned models make their decisions across the distribution of instances. One way to do this is to identify…