110 citations · 174 across the 11 of their papers we have counts for
11 papers
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction
Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones +2
The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identificati…
Coarse-to-Fine Memory Matching for Joint Retrieval and Classification
Allen Schmaltz, Andrew Beam
We present a novel end-to-end language model for joint retrieval and classification, unifying the strengths of bi- and cross- encoders into a single language model via a coarse-to-…
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Benjamin Kompa, Jasper Snoek, Andrew Beam
Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings. Currently, the qualit…
Exemplar Auditing for Multi-Label Biomedical Text Classification
Allen Schmaltz, Andrew Beam
Many practical applications of AI in medicine consist of semi-supervised discovery: The investigator aims to identify features of interest at a resolution more fine-grained than th…
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones +15
This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held…
Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes
Brett K. Beaulieu-Jones, Isaac S. Kohane, Andrew L. Beam
Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts ca…