Rashomon sets alive!

Music City Center 

I will present the Rashomon set paradigm for interpretable machine learning. In this paradigm, machine learning algorithms are not focused on finding a single optimal model, but instead capture the full collection of good (i.e., low-loss) models, i.e., the "Rashomon set." I will show how the Rashomon set paradigm solves the interaction bottleneck to users for sparse decision trees and sparse risk scores, and discuss other benefits of the Rashomon sets discussed in this paper:
Amazing Things Come From Having Many Good Models. ICML spotlight, 2024.
https://arxiv.org/abs/2407.04846

interpretable machine learning

decision trees

sparsity

human-computer interaction

AI