2 papers
cs.LG2026
Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare
Sazan Mahbub, Caleb Ellington, Zhiyuan Li +4
We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesi…
cs.LG2024
Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis
Ethan Wu, Caleb Ellington, Ben Lengerich +1
Tuberculosis (TB) is a major global health challenge, and is compounded by co-morbidities such as HIV, diabetes, and anemia, which complicate treatment outcomes and contribute to h…