4 papers · 1 filter
OrdShap: Feature Position Importance for Sequential Black-Box Models
Davin Hill, Brian L. Hill, Aria Masoomi +3
Sequential deep learning models excel in domains with temporal or sequential dependencies, but their complexity necessitates post-hoc feature attribution methods for understanding…
Axiomatic Explainer Globalness via Optimal Transport
Davin Hill, Josh Bone, Aria Masoomi +2
Explainability methods are often challenging to evaluate and compare. With a multitude of explainers available, practitioners must often compare and select explainers based on quan…
STAR: Stability-Inducing Weight Perturbation for Continual Learning
Masih Eskandar, Tooba Imtiaz, Davin Hill +2
Humans can naturally learn new and varying tasks in a sequential manner. Continual learning is a class of learning algorithms that updates its learned model as it sees new data (on…
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
Zulqarnain Khan, Davin Hill, Aria Masoomi +2
Machine learning methods have significantly improved in their predictive capabilities, but at the same time they are becoming more complex and less transparent. As a result, explai…