4 papers
Context-Adaptive Inference: A Unified Statistical and Foundation-Model View
Yue Yao, Caleb N. Ellington, Jingyun Jia +9
Modern predictive systems are expected to adapt their behavior to the specific situation they are facing. A clinical model should not treat every patient the same; a retrieval-augm…
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…
Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT
Andrew T. McNutt, Abhinav K. Adduri, Caleb N. Ellington +4
Virtual screening of small molecules against protein targets can accelerate drug discovery and development by predicting drug-target interactions (DTIs). However, structure-based m…
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…