4 citations · 7 across the 4 of their papers we have counts for
4 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…
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…
Learning a Generative Model of Cancer Metastasis
Benjamin Kompa, Beau Coker
We introduce a Unified Disentanglement Network (UFDN) trained on The Cancer Genome Atlas (TCGA). We demonstrate that the UFDN learns a biologically relevant latent space of gene ex…
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Andrew L. Beam, Benjamin Kompa, Allen Schmaltz +6
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of…