A Bayesian hypergraph inference method models EHR multi-disease risk by letting risk factors modulate latent hyperedges (disease subsets) with repulsion priors and structured variational inference for uncertainty and scalability.
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A linked Tucker tensor factorization enables a joint individualized hurdle-ordinal regression model that uncovers spatially heterogeneous effects of fluoride and diet on paired caries and fluorosis outcomes.
A Bayesian mixed Hawkes process with Weibull baseline intensity and random effects is developed to model seizure clustering and heterogeneity in focal epilepsy from the Human Epilepsy Project data.
A spectral basis truncation in space and quadrature in time is analyzed for approximating fractional stochastic evolution equations, with strong error bounds proved and verified numerically.
Framework infers sensitive attributes from auxiliary features to enforce fairness constraints in high-dimensional GLMs while aiming to keep predictive performance intact.
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Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
A Bayesian hypergraph inference method models EHR multi-disease risk by letting risk factors modulate latent hyperedges (disease subsets) with repulsion priors and structured variational inference for uncertainty and scalability.
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Linked-Tucker Factorized Individualized Regression for Paired Multivariate Categorical Outcomes
A linked Tucker tensor factorization enables a joint individualized hurdle-ordinal regression model that uncovers spatially heterogeneous effects of fluoride and diet on paired caries and fluorosis outcomes.
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A Mixed Self-Exciting Process to Model Epileptic Seizures
A Bayesian mixed Hawkes process with Weibull baseline intensity and random effects is developed to model seizure clustering and heterogeneity in focal epilepsy from the Human Epilepsy Project data.
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Spectral approximation of a new class of stochastic fractional evolution equations
A spectral basis truncation in space and quadrature in time is analyzed for approximating fractional stochastic evolution equations, with strong error bounds proved and verified numerically.
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Fairness Constraints in High-Dimensional Generalized Linear Models
Framework infers sensitive attributes from auxiliary features to enforce fairness constraints in high-dimensional GLMs while aiming to keep predictive performance intact.