9 citations · 24 across the 8 of their papers we have counts for
5 papers · 1 filter
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization
Yuanlu Bai, Henry Lam, Svitlana Vyetrenko +1
Multi-agent simulation is commonly used across multiple disciplines, specifically in artificial intelligence in recent years, which creates an environment for downstream machine le…
Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems
Mansur Arief, Yuanlu Bai, Wenhao Ding +4
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events. These techniques often…
Over-Conservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation
Yuanlu Bai, Zhiyuan Huang, Henry Lam +1
In rare-event simulation, an importance sampling (IS) estimator is regarded as efficient if its relative error, namely the ratio between its standard deviation and mean, is suffici…
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Yuanlu Bai, Tucker Balch, Haoxian Chen +3
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability. To ensure accurate prediction, the model needs to be calibrated and v…
Model Calibration via Distributionally Robust Optimization: On the NASA Langley Uncertainty Quantification Challenge
Yuanlu Bai, Zhiyuan Huang, Henry Lam
We study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge, a model calibration problem under both aleatory and epistemic uncertainties. Our methodology…