3 papers
stat.CO2026
Trajectory-Oriented Optimization Via Adaptive Thompson Sampling And Grid Refinement: A Tutorial With The ADAPTIVE\_TS Package
David O'Gara, Arindam Fadikar, Mickaël Binois +2
Stochastic simulators are increasingly used to expand the frontier of scientific knowledge and inform decision-making across real-world contexts. Simulator calibration, a process b…
stat.ME2026
Staying on Track: Efficient Trajectory Discovery with Adaptive Batch Sampling
Arindam Fadikar, Abby Stevens, Mickael Binois +3
Bayesian optimization (BO) is a powerful framework for estimating parameters of expensive simulation models, particularly in settings where the likelihood is intractable and evalua…
cs.DC2026
Scaled Block Vecchia Approximation for High-Dimensional Gaussian Process Emulation on GPUs
Qilong Pan, Sameh Abdulah, Mustafa Abduljabbar +8
Emulating computationally intensive scientific simulations is crucial for enabling uncertainty quantification, optimization, and informed decision-making at scale. Gaussian Process…