3 citations · 12 across the 8 of their papers we have counts for
9 papers
FACET: Factorized Asymmetric Conditioning for Efficient Transport in High-Fidelity Fluorescence Microscopy Synthesis
Sazan Mahbub, Caleb N. Ellington, Eric P. Xing
Fluorescence microscopy reveals where proteins localize, but only a limited number of proteins can be imaged in the same cell; generating these images from amino-acid sequence and…
Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare
Sazan Mahbub, Caleb N. Ellington, Caleb Ellington +5
We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesi…
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…
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…
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…
Contextualized Machine Learning
Benjamin Lengerich, Caleb N. Ellington, Andrea Rubbi +2
We examine Contextualized Machine Learning (ML), a paradigm for learning heterogeneous and context-dependent effects. Contextualized ML estimates heterogeneous functions by applyin…
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
Jannik Deuschel, Caleb N. Ellington, Yingtao Luo +3
Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models force a tradeoff between accuracy and interpretabilit…
Multi-task Learning of Order-Consistent Causal Graphs
Xinshi Chen, Haoran Sun, Caleb Ellington +2
We consider the problem of discovering related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of…
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
Ben Lengerich, Caleb Ellington, Bryon Aragam +2
Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) identify context-dependent relationships between variables, but the non-convexity induced by the acyclicity…