It’s All in the Mix: Wasserstein Classification and Regression with Mixed Features

Published Online:https://doi.org/10.1287/msom.2023.0738

References

  • Alley M, Biggs M, Hariss R, Herrmann C, Li ML, Perakis G (2023) Pricing for heterogeneous products: Analytics for ticket reselling. Manufacturing Service Oper. Management 25(2):409–426.LinkGoogle Scholar
  • Ban G-Y, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.LinkGoogle Scholar
  • Bastani H, Bayati M (2020) Online decision making with high-dimensional covariates. Oper. Res. 68(1):276–294.LinkGoogle Scholar
  • Behrendt A, Savelsbergh M, Wang H (2023) A prescriptive machine learning method for courier scheduling on crowdsourced delivery platforms. Transportation Sci. 57(4):889–907.LinkGoogle Scholar
  • Ben-Tal A, Ghaoui LE, Nemirovski A (2009) Robust Optimization (Princeton University Press, Princeton, NJ). CrossrefGoogle Scholar
  • Bertsekas D (2009) Convex Optimization Theory, vol. 1 (Athena Scientific, Nashua, NH).Google Scholar
  • Bertsimas D, den Hertog D (2022) Robust and Adaptive Optimization (Dynamic Ideas, Charlestown, MA).Google Scholar
  • Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.LinkGoogle Scholar
  • Bertsimas D, Pauphilet J (2024) Hospital-wide inpatient flow optimization. Management Sci. 70(7):4893–4911.LinkGoogle Scholar
  • Bertsimas D, Delarue A, Jaillet P, Martin S (2019) Travel time estimation in the age of big data. Oper. Res. 67(2):498–515.AbstractGoogle Scholar
  • Bertsimas D, O’Hair A, Relyea S, Silberholz J (2016) An analytics approach to designing combination chemotherapy regimens for cancer. Management Sci. 62(5):1511–1531.LinkGoogle Scholar
  • Blanchet J, Kang Y (2021) Sample out-of-sample inference based on Wasserstein distance. Oper. Res. 69(3):985–1013.LinkGoogle Scholar
  • Blanchet J, Murthy K (2019) Quantifying distributional model risk via optimal transport. Math. Oper. Res. 44(2):565–600.LinkGoogle Scholar
  • Blanchet J, Kang Y, Murthy K (2019) Robust Wasserstein profile inference and applications to machine learning. J. Appl. Probab. 56(3):830–857.CrossrefGoogle Scholar
  • Chan TCY, Mahmood R, O’Connor DL, Stone D, Unger S, Wong RK, Zhu IY (2025) Got (optimal) milk? Pooling donations in human milk banks with machine learning and optimization. Manufacturing Service Oper. Management 27(6):1721–1739.LinkGoogle Scholar
  • Duchi J, Hashimoto T, Namkoong H (2023) Distributionally robust losses for latent covariate mixtures. Oper. Res. 71(2):649–664.LinkGoogle Scholar
  • Feldman J, Zhang DJ, Liu X, Zhang N (2022) Customer choice models vs. machine learning: Finding optimal product displays on Alibaba. Oper. Res. 70(1):309–328.LinkGoogle Scholar
  • Ferreira KJ, Lee BHA, Simchi-Levi D (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.LinkGoogle Scholar
  • Gao R (2023) Finite-sample guarantees for Wasserstein distributionally robust optimization: Breaking the curse of dimensionality. Oper. Res. 71(6):2291–2306.LinkGoogle Scholar
  • Gao R, Kleywegt A (2023) Distributionally robust stochastic optimization with Wasserstein distance. Math. Oper. Res. 48(2):603–655.LinkGoogle Scholar
  • Gao R, Chen X, Kleywegt AJ (2024) Wasserstein distributionally robust optimization and variation regularization. Oper. Res. 72(3):1177–1191.LinkGoogle Scholar
  • Glaeser CK, Fisher M, Su X (2019) Optimal retail location: Empirical methodology and application to practice. Manufacturing Service Oper. Management 21(1):86–102.LinkGoogle Scholar
  • Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, New York).CrossrefGoogle Scholar
  • Kallus N, Udell M (2020) Dynamic assortment personalization in high dimensions. Oper. Res. 68(4):1020–1037.LinkGoogle Scholar
  • Kelly M, Longjohn R, Nottingham K (2017) The UCI machine learning repository. Accessed April 7, 2025, https://archive.ics.uci.edu.Google Scholar
  • Kuhn D, Shafiee S, Wiesemann W (2024) Distributionally robust optimization. Preprint, submitted November 4, https://arxiv.org/abs/2411.02549v1.Google Scholar
  • Kuhn D, Mohajerin Esfahani P, Nguyen VA, Shafieezadeh-Abadeh S (2019) Wasserstein distributionally robust optimization: Theory and applications in machine learning. Oper. Res. Management Sci. Age Analytics 2019(October):130–169.LinkGoogle Scholar
  • Lam H (2019) Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization. Oper. Res. 6(4):1090–1105.Google Scholar
  • Lee YT, Sidford A, Wong SC-W (2015) A faster cutting plane method and its implications for combinatorial and convex optimization. Ostrovsk R, Guruswami V, eds. 2015 IEEE 56th Annual Sympos. Foundations Comput. Sci. (IEEE Computer Society, Berkeley), 1049–1065.Google Scholar
  • Li R, Tobey M, Mayorga ME, Caltagirone S, Özaltın OY (2023) Detecting human trafficking: Automated classification of online customer reviews of massage businesses. Manufacturing Service Oper. Management 25(3):1051–1065.LinkGoogle Scholar
  • Lubin M, Dowson O, Garcia JD, Huchette J, Legat B, Vielma JP (2023) JuMP 1.0: Recent improvements to a modeling language for mathematical optimization. Math. Programming Comput. 15(3):581–589.CrossrefGoogle Scholar
  • Michaud RO (1989) The Markowitz optimization enigma: Is ‘optimized’ optimal? Financial Analysts J. 45(1):31–42.CrossrefGoogle Scholar
  • Mohajerin Esfahani P, Kuhn D (2018) Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Math. Programming 171(1–2):115–166.CrossrefGoogle Scholar
  • Murphy KP (2022) Probabilistic Machine Learning: An Introduction (MIT Press, Cambridge, MA).Google Scholar
  • Qi M, Cao Y, Shen Z-J (2022) Distributionally robust conditional quantile prediction with fixed design. Management Sci. 68(3):1639–1658.LinkGoogle Scholar
  • Rahimian H, Mehrotra S (2022) Frameworks and results in distributionally robust optimization. Open J. Math. Optim. 3:4.Google Scholar
  • Samorani M, Harris SL, Blount LG, Lu H, Santoro MA (2022) Overbooked and overlooked: Machine learning and racial bias in medical appointment scheduling. Manufacturing Service Oper. Management 24(6):2825–2842.LinkGoogle Scholar
  • Selvi A, Belbasi M, Haugh M, Wiesemann W (2022) Wasserstein logistic regression with mixed features. Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, eds. Advances in Neural Information Processing Systems, vol. 35 (Curran Associates, Inc., Red Hook, NY), 16691–16704.Google Scholar
  • Shafieezadeh-Abadeh S, Kuhn D, Mohajerin Esfahani P (2019) Regularization via mass transportation. J. Machine Learn. Res. 20(103):1–68.Google Scholar
  • Shafieezadeh-Abadeh S, Mohajerin Esfahani P, Kuhn D (2015) Distributionally robust logistic regression. Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 28 (Curran Associates, Inc., Red Hook, NY), 1576–1584.Google Scholar
  • Smith JE, Winkler RL (2006) The optimizer’s curse: Skepticism and postdecision surprise in decision analysis. Management Sci. 52(3):311–322.LinkGoogle Scholar
  • Van Parys BPG, Mohajerin Esfahani P, Kuhn D (2021) From data to decisions: Distributionally robust optimization is optimal. Management Sci. 67(6):3387–3402.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.