Haixiang Lan — accepted AI-conference papers

2025

Dissecting the Impact of Model Misspecification in Data-Driven Optimization

Adam N. Elmachtoub, Henry Lam, Haixiang Lan, Haofeng Zhang

AISTATS 2025poster

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 distributional model which is then plugged into the target optimization problem. While this fitting can utilize tradition…

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2025

The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective

Haixiang Lan, Luofeng Liao, Adam N. Elmachtoub, Christian Kroer, Henry Lam, Haofeng Zhang

NeurIPS 2025poster

Data-driven stochastic optimization is ubiquitous in machine learning and operational decision-making problems. Sample average approximation (SAA) and model-based approaches such as estimate-then-optimize (ETO) or integrated estimation-optimization (IEO) are all popular, with model-based approaches…

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