4 papers
Enhancing Statistical-Computational Balance in Uncertainty Quantification of Bagging: A Cheap Bootstrap Approach
Arindam RoyChowdhury, Henry Lam
Bagging, which refers to the repeated subsampling of data and averaging the resulting outputs, is a common approach to reduce sampling variability of unstable learning and data-dri…
Safe Start: Configuring Optimization Algorithms for Decision-Making under Extreme Risks
Henry Lam, Wasin Meesena
We consider stochastic optimization where the goal is not only to optimize an average-case objective, but also to mitigate the occurrence of rare catastrophic events. This problem…
A Min-Max Gradient Search Method for Constrained Simulation Optimization
Ruiyang Jin, Siyang Gao, Henry Lam
Constrained simulation optimization (CSO) is a general framework for optimizing stochastic systems under performance constraints. It arises widely in practice where objective and c…
Statistically Optimal Uncertainty Quantification for Expensive Black-Box Models
Shengyi He, Henry Lam
Uncertainty quantification, by means of confidence interval (CI) construction, has been a fundamental problem in statistics and also important in risk-aware decision-making. In thi…