8 citations · 21 across the 10 of their papers we have counts for
5 papers · 1 filter
The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective
Haixiang Lan, Luofeng Liao, Adam N. Elmachtoub +3
Data-driven stochastic optimization is ubiquitous in machine learning and operational decision-making problems. Sample average approximation (SAA) and model-based approaches such a…
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits
Ziyi Huang, Henry Lam, Haofeng Zhang
Bayesian bandit algorithms with approximate Bayesian inference have been widely used in real-world applications. Despite the superior practical performance, their theoretical justi…
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
Ziyi Huang, Henry Lam, Haofeng Zhang
Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine learning models. In deep learning, uncertainties arise not only from data, but al…
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…
Learning Prediction Intervals for Regression: Generalization and Calibration
Haoxian Chen, Ziyi Huang, Henry Lam +2
We study the generation of prediction intervals in regression for uncertainty quantification. This task can be formalized as an empirical constrained optimization problem that mini…