9 citations · 24 across the 8 of their papers we have counts for
6 papers · 1 filter
Uncertainty Quantification and Confidence Intervals for Naive Rare-Event Estimators
Yuanlu Bai, Henry Lam
We consider the estimation of rare-event probabilities using sample proportions output by naive Monte Carlo or collected data. Unlike using variance reduction techniques, this naiv…
A Distributionally Robust Optimization Framework for Extreme Event Estimation
Yuanlu Bai, Henry Lam, Xinyu Zhang
Conventional methods for extreme event estimation rely on well-chosen parametric models asymptotically justified from extreme value theory (EVT). These methods, while powerful and…
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…
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…
A Distributionally Robust Optimization Approach to the NASA Langley Uncertainty Quantification Challenge
Yuanlu Bai, Zhiyuan Huang, Henry Lam
We study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge problem, based on an integration of robust optimization, more specifically a recent line of r…