4 papers
Quantification and Decomposition of Uncertainty Using Sliced-Normal Distribution: With Applications to NASA Data
Arindam RoyChowdhury, Luis G. Crespo, Henry Lam
Modeling multivariate distributions with nonlinear dependence, multimodality, and tractable analytical structure for downstream applications is a central challenge in uncertainty q…
Near-Oracle Robustification of Finite-Difference Stochastic Gradient Estimators via Cheap Pilot Calibration
Haidong Li, Henry Lam, Yijie Peng
We study stochastic gradient estimation in black-box environments where only noisy simulation observations of function values are available. Finite-difference (FD) methods are amon…
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
Garud Iyengar, Henry Lam, Tianyu Wang
In data-driven optimization, the sample performance of the obtained decision typically incurs an optimistic bias against the true performance, a phenomenon commonly known as the Op…
Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective
Adam N. Elmachtoub, Henry Lam, Haofeng Zhang +1
In data-driven stochastic optimization, model parameters of the underlying distribution need to be estimated from data in addition to the optimization task. Recent literature consi…