Robust Actionable Prescriptive Analytics
References
- (2024) Strong optimal classification trees. Oper. Res., ePub ahead of print July 31, https://doi.org/10.1287/opre.2021.0034.Link, Google Scholar
- (2020) Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inform. Fusion 58:82–115.Crossref, Google Scholar
- (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
- (2004) Adjustable robust solutions of uncertain linear programs. Math. Programming 99(2):351–376.Crossref, Google Scholar
- (2016) Duality in two-stage adaptive linear optimization: Faster computation and stronger bounds. INFORMS J. Comput. 28(3):500–511.Link, Google Scholar
- (2017) Optimal classification trees. Machine Learn. 106(7):1039–1082.Crossref, Google Scholar
- (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Link, Google Scholar
- (2022) Data-driven optimization: A reproducing kernel Hilbert space approach. Oper. Res. 70(1):454–471.Link, Google Scholar
- (2021) The voice of optimization. Machine Learn. 110(2):249–277.Crossref, Google Scholar
- (2021) Bootstrap robust prescriptive analytics. Math. Programming 195(1):39–78.Google Scholar
- (2019a) Optimal prescriptive trees. INFORMS J. Optim. 1(2):164–183.Link, Google Scholar
- (2023a) Dynamic optimization with side information. Eur. J. Oper. Res. 304(2):634–651.Crossref, Google Scholar
- (2023b) A data-driven approach to multistage stochastic linear optimization. Management Sci. 69(1):51–74.Link, Google Scholar
- (2019b) Adaptive distributionally robust optimization. Management Sci. 65(2):604–618.Link, Google Scholar
- (2019) Robust Wasserstein profile inference and applications to machine learning. J. Appl. Probab. 56(3):830–857.Crossref, Google Scholar
- (2024) Robust CARA optimization. Oper. Res., ePub ahead of print February 26, https://doi.org/10.1287/opre.2021.0654.Google Scholar
- (2025) Robust data-driven CARA optimization. Preprint, submitted February 24, https://doi.org/10.2139/ssrn.5130565.Google Scholar
- (2020) Robust stochastic optimization made easy with RSOME. Management Sci. 66(8):3329–3339.Link, Google Scholar
- (2008) A linear decision-based approximation approach to stochastic programming. Oper. Res. 56(2):344–357.Link, Google Scholar
- (2023) Dual approach for two-stage robust nonlinear optimization. Oper. Res. 71(5):1794–1799.Link, Google Scholar
- (2018) Catboost: Gradient boosting with categorical features support. Preprint, submitted August 24, https://arxiv.org/abs/1810.11363.Google Scholar
- (2022) Smart “predict, then optimize.” Management Sci. 68(1):9–26.Link, Google Scholar
- (2021) Distributionally robust stochastic programs with side information based on trimmings. Math. Programming 195(1):1069–1105.Google Scholar
- (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.Link, Google Scholar
- (2015) On the rate of convergence in Wasserstein distance of the empirical measure. Probab. Theory Related Fields 162(3):707–738.Crossref, Google Scholar
- (2023) Finite-sample guarantees for Wasserstein distributionally robust optimization: Breaking the curse of dimensionality. Oper. Res. 71(6):2291–2306.Link, Google Scholar
- (2024) Wasserstein distributionally robust optimization and variation regularization. Oper. Res. 72(3):1177–1191.Link, Google Scholar
- (2015) Generalized decision rule approximations for stochastic programming via liftings. Math. Programming 152(1):301–338.Crossref, Google Scholar
- (2019) Optimal retail location: Empirical methodology and application to practice. Manufacturing Service Oper. Management 21(1):86–102.Link, Google Scholar
- (2010) Distributionally robust optimization and its tractable approximations. Oper. Res. 58(4):902–917.Link, Google Scholar
- (2020) Empirical asset pricing via machine learning. Rev. Financial Stud. 33(5):2223–2273.Crossref, Google Scholar
- (2010) Nonparametric density estimation for stochastic optimization with an observable state variable. Lafferty J, Williams C, Shawe-Taylor J, Zemel R, Culotta A, eds. Advances in Neural Information Processing Systems, vol. 23 (Curran Associates Inc., Red hook, NY), 820–828.Google Scholar
- (2020) Robust vehicle pre-allocation with uncertain covariates. Production Oper. Management 29(4):955–972.Crossref, Google Scholar
- (2023) Stochastic optimization forests. Management Sci. 69(4):1975–1994.Link, Google Scholar
- (2024) Residuals-based distributionally robust optimization with covariate information. Math. Programming 207(1):369–425.Crossref, Google Scholar
- (2011) Primal and dual linear decision rules in stochastic and robust optimization. Math. Programming 130(1):177–209.Crossref, Google Scholar
- (2018) The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3):31–57.Crossref, Google Scholar
- (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.Crossref, Google Scholar
- (2021) Decision-driven regularization: A blended model for predict-then-optimize. Preprint, submitted June 17, https://doi.org/10.2139/ssrn.3623006.Google Scholar
- (2023) Robust satisficing. Oper. Res. 71(1):61–82.Link, Google Scholar
- (2022) Short-term momentum. Rev. Financial Stud. 35(3):1480–1526.Crossref, Google Scholar
- (2018) Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Math. Programming 171(1–2):115–166.Crossref, Google Scholar
- (2017) Volatility-managed portfolios. J. Finance 72(4):1611–1644.Crossref, Google Scholar
- (2024) Robustifying conditional portfolio decisions via optimal transport. Oper. Res., ePub ahead of print November 4, https://doi.org/10.1287/opre.2021.0243.Link, Google Scholar
- (2022) Prescriptive analytics for flexible capacity management. Management Sci. 68(3):1756–1775.Link, Google Scholar
- (2019) Regularization via mass transportation. J. Machine Learn. Res. 20(103):1–68.Google Scholar
- (2020) Quantifying the empirical Wasserstein distance to a set of measures: Beating the curse of dimensionality. Adv. Neural Inform. Processing Systems 33:21260–21270.Google Scholar
- (2021) A new perspective on supervised learning via robust satisficing. Preprint, submitted December 14, https://doi.org/10.2139/ssrn.3981205.Google Scholar
- (2024) The analytics of robust satisficing: Predict, optimize, satisfice, then fortify. Oper. Res., ePub ahead of print October 7, https://doi.org/10.1287/opre.2023.0199.Link, Google Scholar
- (2006) The optimizer’s curse: Skepticism and postdecision surprise in decision analysis. Management Sci. 52(3):311–322.Link, Google Scholar
- (2021) On data-driven prescriptive analytics with side information: A regularized Nadaraya-Watson approach. Preprint, submitted October 10, https://arxiv.org/abs/2110.04855.Google Scholar
- (2013) Machine learning with operational costs. J. Machine Learn. Res. 14(25):1989–2028.Google Scholar
- (2022) Decision-making with side information: A causal transport robust approach. Preprint, submitted October 16, https://optimization-online.org/?p=20639.Google Scholar
- (2024) Optimal robust policy for feature-based newsvendor. Management Sci. 70(4):2315–2329.Link, Google Scholar
- (2018) Adjustable robust optimization via Fourier–Motzkin elimination. Oper. Res. 66(4):1086–1100.Link, Google Scholar
- (2022a) Robust optimization for models with uncertain second-order cone and semidefinite programming constraints. INFORMS J. Comput. 34(1):196–210.Link, Google Scholar
- (2022b) Disjoint bilinear optimization: A two-stage robust optimization perspective. INFORMS J. Comput. 34(5):2410–2427.Link, Google Scholar

