On the Impossibility of Statistically Improving Empirical Optimization: A Second Order Stochastic Dominance Perspective
References
- (2007) Stochastic Simulation: Algorithms and Analysis, vol. 57 (Springer Science & Business Media, New York), 10.Crossref, Google Scholar
- (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
- (2015) Data-driven stochastic programming using phi-divergences. INFORMS TutORials in Operations Research (INFORMS, Cantonsville, MD), 1–19.Link, Google Scholar
- (2009) Robust Optimization (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
- (2013) Robust solutions of optimization problems affected by uncertain probabilities. Management Sci. 59(2):341–357.Link, Google Scholar
- (2011) Theory and applications of robust optimization. SIAM Rev. 53(3):464–501.Crossref, Google Scholar
- (2018) Robust sample average approximation. Math. Programming 171(1–2):217–282.Crossref, Google Scholar
- (2023) How big should your data really be? Data-driven newsvendor: Learning one sample at a time. Management Sci. 69(10):5848–5865.Google Scholar
- (2011) Introduction to Stochastic Programming (Springer Science & Business Media, New York).Crossref, Google Scholar
- (2019) Quantifying distributional model risk via optimal transport. Math. Oper. Res. 44(2):565–600.Link, Google Scholar
- (2020) On distributionally robust extreme value analysis. Extremes 23(2):317–347.Crossref, Google Scholar
- (2019) Robust Wasserstein profile inference and applications to machine learning. J. Appl. Probab. 56(3):830–857.Crossref, Google Scholar
- (2018) A robust learning approach for regression models based on distributionally robust optimization. J. Machine Learn. Res. 19(13):1–48. Google Scholar
- (2021) The discrete moment problem with nonconvex shape constraints. Oper. Res. 69(1):279–296.Link, Google Scholar
- (2022) Distributionally robust linear and discrete optimization with marginals. Distributionally robust linear and discrete optimization with marginals. Oper. Res. 70(3):1822–1834.Google Scholar
- (2008) Solving operational statistics via a Bayesian analysis. Oper. Res. Lett. 36(1):110–116.Crossref, Google Scholar
- (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58(3):595–612.Link, Google Scholar
- (2021) Worst-case expected shortfall with univariate and bivariate marginals. INFORMS J. Comput. 33(1):370–389.Link, Google Scholar
- (2015) Robustness to dependency in portfolio optimization using overlapping marginals. Oper. Res. 63(6):1468–1488.Link, Google Scholar
- (2017) Task-based end-to-end model learning in stochastic optimization. Adv. Neural Inform. Processing Systems (Long Beach, CA), vol. 30. Google Scholar
- (2019) Variance-based regularization with convex objectives. J. Machine Learn. Res. 20:68–61.Google Scholar
- (2021) Asymptotic optimality in stochastic optimization. Ann. Statist. 49(1):21–48.Crossref, Google Scholar
- (2021) Statistics of robust optimization: A generalized empirical likelihood approach. Math. Oper. Res. 46(3):946–969.Link, Google Scholar
- (2016) Path-space information bounds for uncertainty quantification and sensitivity analysis of stochastic dynamics. SIAM/ASA J. Uncertainty Quant. 4(1):80–111.Crossref, Google Scholar
- (2003) Worst-case value-at-risk and robust portfolio optimization: A conic programming approach. Oper. Res. 51(4):543–556.Link, Google Scholar
- (2022) Smart “predict, then optimize.” Management Sci. 68(1):9–26.Link, Google Scholar
- (2023) Estimate-then-optimize versus integrated-estimation-optimization: A stochastic dominance perspective. Preprint, submitted April 13, https://arxiv.org/abs/2304.06833.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
- (2020) Distributionally robust selection of the best. Management Sci. 66(1):190–208.Link, Google Scholar
- (2023) The framework of parametric and nonparametric operational data analytics. Production Oper. Management 32(9):2685–2703.Crossref, Google Scholar
- (2001) The Elements of Statistical Learning, vol. 1 (Springer Series in Statistics, New York).Google Scholar
- (2023) Distributionally robust stochastic optimization with Wasserstein distance. Math. Oper. Res. 48(2):603–655.Link, Google Scholar
- (2024) Wasserstein distributionally robust optimization and variation regularization. Oper. Res. 72(3):1177–1191.Link, Google Scholar
- (2019) Robust analysis in stochastic simulation: Computation and performance guarantees. Oper. Res. 67(1):232–249.Link, Google Scholar
- (2014) Robust risk measurement and model risk. Quant. Finance 14(1):29–58.Crossref, Google Scholar
- (2010) Distributionally robust optimization and its tractable approximations. Oper. Res. 58(4):902–917.Link, Google Scholar
- (2018) Robust empirical optimization is almost the same as mean–variance optimization. Oper. Res. Lett. 46(4):448–452.Crossref, Google Scholar
- (2021) Calibration of distributionally robust empirical optimization models. Oper. Res. 69(5):1630–1650.Link, Google Scholar
- (2019) Near-optimal Bayesian ambiguity sets for distributionally robust optimization. Management Sci. 65(9):4242–4260.Link, Google Scholar
- (2021) Data pooling in stochastic optimization. Management Sci. 68(3):1595–1615.Google Scholar
- (2021) Small-data, large-scale linear optimization with uncertain objectives. Management Sci. 67(1):220–241.Link, Google Scholar
- (1969) Rules for ordering uncertain prospects. Amer. Econom. Rev. 59(1):25–34.Google Scholar
- (1974) The influence curve and its role in robust estimation. J. Amer. Statist. Assoc. 69(346):383–393.Crossref, Google Scholar
- (2015) A distributionally robust perspective on uncertainty quantification and chance constrained programming. Math. Programming 151(1):35–62.Crossref, Google Scholar
- (1969) The efficiency analysis of choices involving risk. Rev. Econom. Stud. 36(3):335–346.Crossref, Google Scholar
- (2008) Robustness (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
- (1996) Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming, vol. 8 (Springer Science & Business Media, Dordrecht, Netherlands).Crossref, Google Scholar
- (2020) Learning-based robust optimization: Procedures and statistical guarantees. Management Sci. 67(6):3447–3467.Google Scholar
- (2022) Fast rates for contextual linear optimization. Management Sci. 68(6):4236–4245.Link, Google Scholar
- (2005) Robust dynamic programming. Math. Oper. Res. 30(2):257–280.Link, Google Scholar
- (2023) Hedging against complexity: Distributionally robust optimization with parametric approximation. Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 9976–10011.Google Scholar
- (2016) Data-driven chance constrained stochastic program. Math. Programming 158(1):291–327.Crossref, Google Scholar
- (2007) Introduction to Empirical Processes and Semiparametric Inference (Springer Science & Business Media, New York).Google Scholar
- (2019) Wasserstein distributionally robust optimization: Theory and applications in machine learning. INFORMS TutORials in Operations Research (INFORMS, Cantonsville, MD), 130–166.Link, Google Scholar
- (2016) Robust sensitivity analysis for stochastic systems. Math. Oper. Res. 41(4):1248–1275.Link, Google Scholar
- (2018) Sensitivity to serial dependency of input processes: A robust approach. Management Sci. 64(3):1311–1327.Link, Google Scholar
- (2019) Recovering best statistical guarantees via the empirical divergence-based distributionally robust optimization. Oper. Res. 67(4):1090–1105.Abstract, Google Scholar
- (2017) Tail analysis without parametric models: A worst-case perspective. Oper. Res. 65(6):1696–1711.Link, Google Scholar
- (2017) The empirical likelihood approach to quantifying uncertainty in sample average approximation. Oper. Res. Lett. 45(4):301–307.Crossref, Google Scholar
- (1993) Mean-preserving portfolio dominance. Rev. Econom. Stud. 60(2):479–485.Crossref, Google Scholar
- (1986) Asymptotic optimality of CL and generalized cross-validation in ridge regression with application to spline smoothing. Ann. Statist. 14(3):1101–1112.Crossref, Google Scholar
- (1987) Asymptotic optimality for Cp, CL, cross-validation and generalized cross-validation: Discrete index set. Ann. Statist. 15(3):958–975. Crossref, Google Scholar
- (2019) Ambiguous risk constraints with moment and unimodality information. Math. Programming 173:151–192.Crossref, Google Scholar
- (2006) Model uncertainty, robust optimization, and learning. INFORMS TutORials in Operations Research (INFORMS, Cantonsville, MD), 66–94.Link, Google Scholar
- (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.Crossref, Google Scholar
- (2000) Minimax optimal control of stochastic uncertain systems with relative entropy constraints. IEEE Trans. Automatic Control 45(3):398–412.Crossref, Google Scholar
- (2012) Nonparametric divergence estimation with applications to machine learning on distributions. Preprint, submitted February 14, https://arxiv.org/abs/1202.3758.Google Scholar
- (2005) A semidefinite programming approach to optimal-moment bounds for convex classes of distributions. Math. Oper. Res. 30(3):632–657.Link, Google Scholar
- (2022) Integrating prediction/estimation and optimization with applications in operations management. INFORMS TutORials in Operations Research (INFORMS, Cantonsville, MD), 36–58.Link, Google Scholar
- (2019) Distributionally robust optimization: A review. Preprint, submitted August 13, https://arxiv.org/abs/1908.05659.Google Scholar
- (2015) Convex Analysis (Princeton University Press, Princeton, NJ).Google Scholar
- (1970) Increasing risk: I. A definition. J. Econom. Theory 2(3):225–243.Crossref, Google Scholar
- (2025) Do robustness and accuracy compete? Symmetry of uncertainty and superiority of naive optimization. Preprint.Google Scholar
- (2025) A survey of contextual optimization methods for decision-making under uncertainty. Eur. J. Oper. Res. 320(2):271–289.Crossref, Google Scholar
- (2009) Approximation Theorems of Mathematical Statistics, vol. 162 (John Wiley & Sons, New York), 10.Google Scholar
- (2019) Regularization via mass transportation. J. Machine Learn. Res. 20(103):1–68.Google Scholar
- (2007) Stochastic Orders (Springer Science & Business Media, New York).Crossref, Google Scholar
- (1989) Asymptotic properties of statistical estimators in stochastic programming. Ann. Statist. 17(2):841–858.Crossref, Google Scholar
- (2014) Lectures on Stochastic Programming: Modeling and Theory (SIAM, Philadelphia).Crossref, Google Scholar
- (2012) On the empirical estimation of integral probability metrics. Electronic J. Statist. 6:1550–1599.Crossref, Google Scholar
- (2020) A general framework for optimal data-driven optimization. Preprint, submitted October 13, https://arxiv.org/abs/2010.06606.Google Scholar
- (2000) Asymptotic Statistics, vol. 3 (Cambridge University Press, Cambridge, UK).Google Scholar
- (1996) Weak Convergence and Empirical Processes (Springer, New York).Crossref, Google Scholar
- (2020) From data to decisions: Distributionally robust optimization is optimal. Management Sci. 67(6):3387–3402.Google Scholar
- (2016) Generalized Gauss inequalities via semidefinite programming. Math. Programming 156(1–2):271–302.Crossref, Google Scholar
- (2009) Divergence estimation for multidimensional densities via k-nearest-neighbor distances. IEEE Trans. Inform. Theory 55(5):2392–2405.Crossref, Google Scholar
- (2016) Likelihood robust optimization for data-driven problems. Comput. Management Sci. 13(2):241–261.Crossref, Google Scholar
- (2014) Distributionally robust convex optimization. Oper. Res. 62(6):1358–1376.Link, Google Scholar
- (2019) End to end learning and optimization on graphs. Adv. Neural Inform. Processing Systems (Vancouver), vol. 32.Google Scholar
- (2018) A Bayesian risk approach to data-driven stochastic optimization: Formulations and asymptotics. SIAM J. Optim. 28(2):1588–1612.Crossref, Google Scholar
- (2012) Distributionally robust Markov decision processes. Math. Oper. Res. 37(2):288–300.Link, Google Scholar
- (2022) Generalization bounds with minimal dependency on hypothesis class via distributionally robust optimization. Adv. Neural Inform. Processing Systems (New Orleans), vol. 35, 27576–27590.Google Scholar

