| On the need for a language describing distribution shifts: Illustrations on tabular datasets J Liu, T Wang, P Cui, H Namkoong Advances in Neural Information Processing Systems 36, 2024 | 13 | 2024 |
| Distributionally robust prescriptive analytics with Wasserstein distance T Wang, N Chen, C Wang arXiv preprint arXiv:2106.05724, 2021 | 9 | 2021 |
| Hedging against complexity: Distributionally robust optimization with parametric approximation G Iyengar, H Lam, T Wang International Conference on Artificial Intelligence and Statistics, 9976-10011, 2023 | 5* | 2023 |
| Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization G Iyengar, H Lam, T Wang arXiv preprint arXiv:2306.10081, 2023 | 4 | 2023 |
| Contextual Optimization under Covariate Shift: A Robust Approach by Intersecting Wasserstein Balls T Wang, N Chen, C Wang arXiv preprint arXiv:2406.02426, 2024 | 1 | 2024 |
| Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications J Liu, J Wu, T Wang, H Zou, B Li, P Cui arXiv preprint arXiv:2311.05054, 2023 | 1 | 2023 |
| Data-Driven Distributionally Robust CVaR Portfolio Optimization Under A Regime-Switching Ambiguity Set CS Pun, T Wang, Z Yan Manufacturing & Service Operations Management 25 (5), 1779-1795, 2023 | 1 | 2023 |
| Is Cross-Validation the Gold Standard to Evaluate Model Performance? G Iyengar, H Lam, T Wang arXiv preprint arXiv:2407.02754, 2024 | | 2024 |
