Proposes nearly balanced TCARDs that minimize the first two generalized word-length pattern components, defines Φ_BCD criterion linked to classical optimality, and constructs designs via coordinate exchange with simulation-calibrated weights for LLM prompt engineering.
Journal of statistical planning and inference , volume=
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CarCrashNet supplies a large multi-modal crash simulation benchmark and CrashSolver neural model for data-driven full-vehicle crash prediction, validated against experiments and commercial solvers.
A cost-aware space-filling input design method using Gaussian processes for nonlinear system identification that reduces experimental cost while preserving model performance.
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.
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TCARD: Nearly Balanced Two-Level Designs with Treatment Cardinality Constraints with an Application to LLM Prompt Engineering
Proposes nearly balanced TCARDs that minimize the first two generalized word-length pattern components, defines Φ_BCD criterion linked to classical optimality, and constructs designs via coordinate exchange with simulation-calibrated weights for LLM prompt engineering.
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REX-SUB: A Scalable Subsampling Strategy for Modeling Large Spatial Datasets
REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.