Deep Learning Methods for Proximal Inference via Maximum Moment Restriction

1 code implementation • 19 May 2022 • Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew L. Beam

In this work, we introduce a flexible and scalable method based on a deep neural network to estimate causal effects in the presence of unmeasured confounding using proximal inference.

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

1 code implementation • 6 Oct 2020 • 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.

Uncertainty Quantification

Learning a Generative Model of Cancer Metastasis

1 code implementation • 17 Jan 2019 • Benjamin Kompa, Beau Coker

We demonstrate that our interpolations learn relevant metagenes that recapitulate known glioblastoma mechanisms and suggest possible starting points for investigations into the metastasis of SKCM into GBM.

Disentanglement General Classification

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

4 code implementations • 4 Apr 2018 • Andrew L. Beam, Benjamin Kompa, Allen Schmaltz, Inbar Fried, Griffin Weber, Nathan P. Palmer, Xu Shi, Tianxi Cai, Isaac S. Kohane

Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing.

Word Embeddings