On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya–Watson Approach
References
- (2009) Relationship between interest rate and stock price: Empirical evidence from developed and developing countries. Internat. J. Bus. Management 4(3):43–51.Google Scholar
- (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
- (2019) Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing Service Oper. Management 21(4):798–815.Link, Google Scholar
- (2020) Generalization bounds for regularized portfolio selection with market side information. INFOR Inform. Systems Oper. Res. 58(2):374–401.Crossref, Google Scholar
- (2025) Learning and decision-making with data: Optimal formulations and phase transitions. Math. Programming, 1–93.Google Scholar
- (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Link, Google Scholar
- (2019) From predictions to prescriptions in multistage optimization problems. Preprint, submitted April 26, https://arxiv.org/abs/1904.11637.Google Scholar
- (2022) Bootstrap robust prescriptive analytics. Math. Programming 195(1):39--78.Google Scholar
- (2019) Dynamic optimization with side information. Preprint, submitted July 17, https://arxiv.org/abs/1907.07307.Google Scholar
- (2018) Making the world more sustainable: Enabling localized energy generation and distribution on decentralized smart grid systems. World J. Engrg. Tech. 6(2):350–382.Crossref, Google Scholar
- (2009) Parametric portfolio policies: Exploiting characteristics in the cross-section of equity returns. Rev. Financial Stud. 22(9):3411–3447.Crossref, Google Scholar
- (2009) Multi-view clustering via canonical correlation analysis. Proc. 26th Annual Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 129–136.Google Scholar
- (2008) Dynamic inventory management with learning about the demand distribution and substitution probability. Manufacturing Service Oper. Management 10(2):236–256.Link, Google Scholar
- (2022) A statistical learning approach to personalization in revenue management. Management Sci. 68(3):1923–1937.Link, Google Scholar
- (2005) Locational marginal price sensitivities. IEEE Trans. Power Systems 20(4):2026–2033.Crossref, Google Scholar
- (1998) Large Deviations Techniques and Applications, Stochastic Modelling and Applied Probability, vol. 38 (Springer, Berlin).Crossref, Google Scholar
- (2019) Variance-based regularization with convex objectives. J. Machine Learn. Res. 20(1):2450–2504.Google Scholar
- (2003) Moderate deviations for I.I.D. random variables. ESAIM Probab. Statist. 7:209–218.Crossref, Google Scholar
- (2019) Generalization bounds in the predict-then-optimize framework. Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 32 (Curran Associates, Red Hook, NY).Google Scholar
- (2022) Smart “predict, then optimize”. Management Sci. 68(1):9–26.Link, Google Scholar
- (2022) Distributionally robust stochastic programs with side information based on trimmings. Math. Programming 195(1):1069–1105.Google Scholar
- (2023) Smart predict-then-optimize for two-stage linear programs with side information. INFORMS J. Optim. 5(3):295–320.Google Scholar
- (2001) Classes of kernels for machine learning: A statistics perspective. J. Machine Learn. Res. 2:299–312.Google Scholar
- (2018) Robust empirical optimization is almost the same as mean–variance optimization. Oper. Res. Let. 46(4):448–452.Crossref, Google Scholar
- (2019) Near-optimal Bayesian ambiguity sets for distributionally robust optimization. Management Sci. 65(9):4242–4260.Link, Google Scholar
- Gurobi Optimization, LLC (2024) Gurobi Optimizer reference manual. https://www.gurobi.com.Google Scholar
- (2006) A Distribution-Free Theory of Nonparametric Regression (Springer, New York).Google Scholar
- (2013) Robust data-driven dynamic programming. Burges CJ, Bottou L, Welling M, Ghahramani Z, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 26 (Curran Associates, Red Hook, NY), 827–835.Google Scholar
- (2011) Approximate dynamic programming for storage problems. Proc. 28th Internat. Conf. Machine Learn. (Omnipress, Madison, WI), 337–344.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, Red Hook, NY), 820–828.Google Scholar
- (2022) Risk guarantees for end-to-end prediction and optimization processes. Management Sci. 68(12):8680–8698.Link, Google Scholar
- (2022) Fast rates for contextual linear optimization. Management Sci. 68(6):4236–4245.Google Scholar
- (2024) Residuals-based distributionally robust optimization with covariate information. Math. Programming 207(1):369–425. Google Scholar
- (2025) Technical note--data-driven sample average approximation with covariate information. Oper. Res. 73(6):3245–3259.Google Scholar
- (2021) Heteroscedasticity-aware residuals-based contextual stochastic optimization. Preprint, submitted January 8, https://arxiv.org/abs/2101.03139.Google Scholar
- (2011) Optimal energy commitments with storage and intermittent supply. Oper. Res. 59(6):1347–1360.Link, Google Scholar
- (2002) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2):479–502.Crossref, Google Scholar
- (2016) Robust sensitivity analysis for stochastic systems. Math. Oper. Res. 41(4):1248–1275.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
- (2020) Decision-driven regularization: Harmonizing the predictive and prescriptive. Preprint, submitted June 17, https://doi.org/10.2139/ssrn.3623006.Google Scholar
- (2009) Empirical Bernstein bounds and sample variance penalization. Preprint, submitted July 21, https://arxiv.org/abs/0907.3740.Google Scholar
- (2011) Optimization: Foundations and Applications (John Wiley & Sons, Hoboken, NJ).Google Scholar
- (2008) Large and moderate deviations principles for kernel estimators of the multivariate regression. Math. Methods Statist. 17(2):146–172.Crossref, Google Scholar
- (1964) On estimating regression. Theory Probab. Its Appl. 9(1):141–142.Crossref, Google Scholar
- (1962) On estimation of a probability density function and mode. Ann. Math. Statist. 33(3):1065–1076.Crossref, Google Scholar
- (2011) Scikit-learn: Machine learning in Python. J. Machine Learn. Res. 12(85):2825–2830. Google Scholar
- (2018) Learning enabled optimization: Towards a fusion of statistical learning and stochastic programming. Technical report, University of Southern California, Los Angeles.Google Scholar
- (2009) Lectures on Stochastic Programming: Modeling and Theory (Society For Industrial and Applied Mathematics, Philadelphia).Crossref, Google Scholar
- (1986) Density Estimation for Statistics and Data Analysis, Monographs on Statistics and Applied Probability, vol. 26 (Chapman & Hall, London).Google Scholar
- (2021) The analytics of robust satisficing—Predict, optimise, satisfice, then fortify. Preprint, submitted April 20, https://doi.org/10.2139/ssrn.3829562.Google Scholar
- (2019) A robust spectral clustering algorithm for sub-Gaussian mixture models with outliers. Preprint, submitted December 16, https://arxiv.org/abs/1912.07546.Google Scholar
- (2010) Introduction to the non-asymptotic analysis of random matrices. Preprint, submitted November 12, https://arxiv.org/abs/1011.3027.Google Scholar
- (2019) High-Dimensional Statistics: A Non-Asymptotic Viewpoint, Cambridge Series in Statistical and Probabilistic Mathematics, vol. 48 (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2013) The effect of weather on grid systems and the reliability of electricity supply. Climatic Change 121(1):103–113.Crossref, Google Scholar
- (1964) Smooth regression analysis. Sankhyā Indian J. Statist. Ser. A 26(4):359–372.Google Scholar
- , NDLAS team (2013) Overview of the North American Land Data Assimilation System (NLDAS). Land Surface Observation, Modeling and Data Assimilation (World Scientific, Singapore), 337–377.Crossref, Google Scholar
- (2016) Statistical optimization in high dimensions. Oper. Res. 64(4):958–979.Link, Google Scholar
- (2021) Covariate regularized community detection in sparse graphs. J. Amer. Statist. Assoc. 116(534):734–745.Crossref, Google Scholar

