Search Results for author:
Found 6 papers, 2 papers with code
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death.
Autoencoded sparse Bayesian in-IRT factorization, calibration, and amortized inference for the Work Disability Functional Assessment Battery
We also analogize our multidimensional IRT model to probabilistic autoencoders, specifying an encoder function that amortizes the inference of ability parameters from item responses.
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death
We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks.
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
However, sparsity in representation decoding does not necessarily imply sparsity in the encoding of representations from the original data features.
Probabilistically-autoencoded horseshoe-disentangled multidomain item-response theory models
By binding the generative IRT model to a Bayesian neural network (forming a probabilistic autoencoder), one obtains a scoring algorithm consistent with the interpretable Bayesian model.
Before and beyond the Wilson-Cowan equations
The Wilson-Cowan equations represent a landmark in the history of computational neuroscience.
Neurons and Cognition
