8 citations · 21 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Evaluating Aleatoric Uncertainty via Conditional Generative Models
Ziyi Huang, Henry Lam, Haofeng Zhang
Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learn…
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
Ziyi Huang, Henry Lam, Amirhossein Meisami +1
Bayesian bandit algorithms with approximate Bayesian inference have been widely used in real-world applications. However, there is a large discrepancy between the superior practica…
Quantifying Epistemic Uncertainty in Deep Learning
Ziyi Huang, Henry Lam, Haofeng Zhang
Uncertainty quantification is at the core of the reliability and robustness of machine learning. In this paper, we provide a theoretical framework to dissect the uncertainty, espec…