An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
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Optimization Monte Carlo reformulates stochastic simulator inference as gradient-based deterministic optimization for faster, accurate posterior estimation in high-dimensional or challenging settings.
PATCH model simulations show preferential attachment and homophily increase segregation and degree inequality while triadic closure reduces segregation but amplifies overall inequality, and the model accounts for observed gender disparities in 50 years of physics and CS collaboration networks.
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Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
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Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
Optimization Monte Carlo reformulates stochastic simulator inference as gradient-based deterministic optimization for faster, accurate posterior estimation in high-dimensional or challenging settings.
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Network Inequality through Preferential Attachment, Triadic Closure, and Homophily
PATCH model simulations show preferential attachment and homophily increase segregation and degree inequality while triadic closure reduces segregation but amplifies overall inequality, and the model accounts for observed gender disparities in 50 years of physics and CS collaboration networks.