2 citations · 3 across the 3 of their papers we have counts for
3 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★ 1 cited
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…
cs.LG2012★ 2 cited
Learning Hidden Markov Models for Regression using Path Aggregation
Keith Noto, Mark Craven
We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for reg…