14 citations · 33 across the 5 of their papers we have counts for
5 papers
DAG-aware Transformer for Causal Effect Estimation
Manqing Liu, David R. Bellamy, Andrew L. Beam
Causal inference is a critical task across fields such as healthcare, economics, and the social sciences. While recent advances in machine learning, especially those based on the d…
Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
David R. Bellamy, Bhawesh Kumar, Cindy Wang +1
In this work we introduce Labrador, a pre-trained Transformer model for laboratory data. Labrador and BERT were pre-trained on a corpus of 100 million lab test results from electro…
Conformal Prediction with Large Language Models for Multi-Choice Question Answering
Bhawesh Kumar, Charlie Lu, Gauri Gupta +4
As large language models continue to be widely developed, robust uncertainty quantification techniques will become crucial for their safe deployment in high-stakes scenarios. In th…
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…
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data
David Bellamy, Leo Celi, Andrew L. Beam
The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset catalyzed an intense flurry of progress in computer vision. Benchmark tasks have propelled oth…