Exploring the cloud of variable importance for the set of all good models

By Jiayun Dong, Cynthia Rudin. 2020

74 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical Learning Theory

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. Lesia Semenova
2. Chudi Zhong
3. Zhi Chen
4. Haiyang Huang
5. Cynthia Rudin
6. Tong Wang
7. Octavio Loyola-González
8. Berk Ustun

In Computer Vision and Pattern Recognition

1. Zhi Chen
2. Lesia Semenova
3. Chudi Zhong
4. Haiyang Huang
5. Cynthia Rudin

In Statistical Learning Theory

1. Chudi Zhong
2. Cynthia Rudin
3. Zhi Chen
4. Haiyang Huang
5. Lesia Semenova
6. Tong Wang
7. Ş. Ertekin
8. Berk Ustun

Citations in the corpus

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “For any user who would pick a “good” model (i.e., a model in the Rashomon set), then the importance of the variable is within the range given by the MCR. Dong and Rudin (2020) expand on this by suggesting to visualize the “cloud” of variable importance values for models within the Rashomon set.”