9 citations · 24 across the 8 of their papers we have counts for
10 papers
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…
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…
Rare-Event Simulation for Neural Network and Random Forest Predictors
Yuanlu Bai, Zhiyuan Huang, Henry Lam +1
We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random fo…
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…
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar +5
Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications. Simulation provides a useful…