14.3k citations · 14.4k across the 6 of their papers we have counts for
6 papers
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death
Joshua C. Chang, Ted L. Chang, Carson C. Chow +5
We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated d…
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
Joshua C. Chang, Patrick Fletcher, Jungmin Han +5
Dimensionality reduction methods for count data are critical to a wide range of applications in medical informatics and other fields where model interpretability is paramount. For…
Probabilistically-autoencoded horseshoe-disentangled multidomain item-response theory models
Joshua C. Chang, Shashaank Vattikuti, Carson C. Chow
Item response theory (IRT) is a non-linear generative probabilistic paradigm for using exams to identify, quantify, and compare latent traits of individuals, relative to their peer…
Multi-scale variability in neuronal competition
Benjamin P Cohen, Carson C Chow, Shashaank Vattikuti
We examine whether a single biophysical cortical circuit model can explain both spiking and perceptual variability. We consider perceptual rivalry, which provides a window into int…
Second-generation PLINK: rising to the challenge of larger and richer datasets
Christopher C. Chang, Carson C. Chow, Laurent C. A. M. Tellier +3
PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imp…
Application of compressed sensing to genome wide association studies and genomic selection
Shashaank Vattikuti, James J. Lee, Christopher C. Chang +2
We show that the signal-processing paradigm known as compressed sensing (CS) is applicable to genome-wide association studies (GWAS) and genomic selection (GS). The aim of GWAS is…