Search Results for author:
Found 3 papers, 2 papers with code
Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data
Machine learning in high-stakes domains, such as healthcare, faces two critical challenges: (1) generalizing to diverse data distributions given limited training data while (2) maintaining interpretability.
On Guiding Visual Attention with Language Specification
To do this, we ground task-relevant words or phrases with attention maps from a pretrained large-scale model.
Fast Interpretable Greedy-Tree Sums
In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
