14.3k citations · 14.4k across the 12 of their papers we have counts for
12 papers
Perturbative adaptive importance sampling for Bayesian LOO cross-validation
Joshua C Chang, Xiangting Li, Tianyi Su +4
Importance sampling (IS) is an efficient stand-in for model refitting in performing (LOO) cross-validation (CV) on a Bayesian model. IS inverts the Bayesian update for a single obs…
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
Hongjing Xia, Joshua C. Chang, Sarah Nowak +4
We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death. Our approach avoids a specif…
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…
Finite size effects for spiking neural networks with spatially dependent coupling
Siwei Qiu, Carson Chow
We study finite-size fluctuations in a network of spiking deterministic neurons coupled with non-uniform synaptic coupling. We generalize a previously developed theory of finite si…
Learning recurrent dynamics in spiking networks
Christopher Kim, Carson Chow
Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform variou…