1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
Stochastic Optimization with Optimal Importance Sampling
Liviu Aolaritei, Bart P. G. Van Parys, Henry Lam +1
Importance Sampling (IS) is a widely used variance reduction technique for enhancing the efficiency of Monte Carlo methods, particularly in rare-event simulation and related applic…
Dissecting the Impact of Model Misspecification in Data-driven Optimization
Adam N. Elmachtoub, Henry Lam, Haixiang Lan +1
Data-driven optimization aims to translate a machine learning model into decision-making by optimizing decisions on estimated costs. Such a pipeline can be conducted by fitting a d…
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…