Publications and Preprints

Working Papers

• Achieving First-order Statistical Improvements in Data-Driven Optimization
  Henry Lam^*, Tianyu Wang ^*
  

  Preliminary version appeared in NeurIPS 2025 OPT Workshop on Optimization for Machine Learning.

• Contextual Optimization under Covariate Shift: A Robust Approach by Intersecting Wasserstein Balls
  Tianyu Wang, Ningyuan Chen, and Chun Wang
  Major revision at Manufacturing and Service Operations Management.
  

  Preliminary version: Distributionally Robust Prescriptive Analytics with Wasserstein Distance

• Optimizing Pharmaceutical Control with Multi-Task Contextual Bandits: Addressing Batch Heterogeneity for Improved Manufacturing Efficiency
  Tianyu Wang, Naz Pinar Taskiran, and Garud Iyengar
  Major revision at Manufacturing and Service Operations Management.
  Finalist of MSOM Data-Driven Research Challenge 2025.

• DRO: A Python Library for Distributionally Robust Optimization in Machine Learning [Website][Code] [Python package]
  Jiashuo Liu⁺, Tianyu Wang⁺, Henry Lam, Hongseok Namkoong, Jose Blanchet
  R&R (aka Major revision) at Journal of Machine Learning Research.
  

• Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework
  Garud Iyengar^*, Henry Lam^*, Tianyu Wang^*
  Major revision at Management Science.

  Preliminary version appeared in AISTATS 2023.

Journal Articles (including minor revisions)

• Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization [Code] [Poster]
  Garud Iyengar^*, Henry Lam^*, Tianyu Wang^*
  Minor revision at Management Science.
  Honorable Mention of Dupačová-Prékopa Best Student Paper Prize in Stochastic Programming 2025.

• Rethinking Distribution Shifts: Empirical Analysis and Inductive Modeling for Tabular Data
  Tianyu Wang⁺, Jiashuo Liu⁺, Peng Cui, Hongseok Namkoong
  Management Science, forthcoming.

  Preliminary version appeared in NeurIPS 2023.

• Data-Driven Distributionally Robust CVaR Portfolio Optimization Under A Regime-Switching Ambiguity Set [Code]
  Chi Seng Pun^*, Tianyu Wang^*, Zhenzhen Yan^*
  Manufacturing and Service Operations Management, 25(5): 1779 - 1795, 2023.

Refereed Conference Publications

• A Hybrid Simulation-Machine Learning Approach to Improve EMS Responses in New York City

  Derek Long, Tianyu Wang, Henry Lam, Jay Sethuraman, Matthew Adams, Monish Dadlani, Kathleen Thomson, Ayten Turkcan et al.
  Winter Simulation Conference (WSC) 2026.

• Integrating Feature Correlation in Differential Privacy with Applications in DP-ERM
  Tianyu Wang, Luhao Zhang, Rachel Cummings
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2026.

• Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? [Code] [Poster]
  Garud Iyengar^*, Henry Lam^*, Tianyu Wang^*
  Advances in Neural Information Processing Systems (NeurIPS) 2024.

• Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
  Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Peng Cui
  International Conference on Machine Learning (ICML) 2024.
  Preliminary version appeared in NeurIPS 2023 Workshop on Distribution Shifts.

• On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets [Code] [Python package]
  Jiashuo Liu⁺, Tianyu Wang⁺, Peng Cui, Hongseok Namkoong
  Advances in Neural Information Processing Systems (NeurIPS) 2023, Datasets and Benchmarks Track.
  Highlighted as NeurIPS 2023 Favorite Papers by Two Sigma (9/3500+) [Link]

• Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation [Code]
  Garud Iyengar^*, Henry Lam^*, Tianyu Wang^*
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
  Notable Paper (Oral presentation), 32/1689 = 1.9% of submissions