Stochastic Optimization Forests

References

• Athey S, Tibshirani J, Wager S (2019) Generalized random forests. Ann. Statist. 47(2):1148–1178.Crossref, Google Scholar
• Bertsekas D (1995) Nonlinear programming (Athena Scientific, Nashua, NH).Google Scholar
• Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Google Scholar
• Biau G, Scornet E (2016) A random forest guided tour. TEST. 25(2):197–227.Crossref, Google Scholar
• Bonnans JF, Shapiro A (2000) Perturbation Analysis of Optimization Problems (Springer, New York).Crossref, Google Scholar
• Breiman L (2001) Random forests. Machine Learn. 45(1):5–32.Crossref, Google Scholar
• Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and Regression Trees (CRC Press, Boca Raton, FL).Google Scholar
• Carroll RJ, Ruppert D, Welsh AH (1998) Local estimating equations. J. Amer. Statist. Assoc. 93(441):214–227.Crossref, Google Scholar
• Chen Z, Leng C (2015) Local linear estimation of covariance matrices via cholesky decomposition. Statist. Sinica 25(3):1249–1263.Google Scholar
• Cornuejols G, Tütüncü R (2006) Optimization Methods in Finance (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Denil M, Matheson D, Freitas ND (2014) Narrowing the gap: Random forests in theory and in practice. Proc. 31st Internat. Conf. on Machine Learn., 665–673.Google Scholar
• Donti P, Amos B, Kolter JZ (2017) Task-based end-to-end model learning in stochastic optimization. Adv. Neural Inform. Processing Systems 30:5484–5494.Google Scholar
• Elmachtoub AN, Grigas P (2022) Smart “predict, then optimize”. Management Sci. 68(1):9–26.Google Scholar
• Elmachtoub AN, Liang JCN, McNellis R (2020) Decision trees for decision-making under the predict-then-optimize framework. Proc. 37th Internat. Conf. Machine Learn. Proc. Machine Learn. Res. vol. 119 (PMLR), 2858–2867.Google Scholar
• Fan J, Yao Q (1998) Efficient estimation of conditional variance functions in stochastic regression. Biometrika 85(3):645–660.Google Scholar
• Fan J, Farmen M, Gijbels I (1998) Local maximum likelihood estimation and inference. J. Royal Statist. Soc. Ser. B Statist. Methodology 60(3):591–608.Crossref, Google Scholar
• Hanasusanto GA, Kuhn D (2013) Robust data-driven dynamic programming. Adv. Neural Inform. Processing Systems 26:827–835.Google Scholar
• Hannah L, Powell W, Blei DM (2010) Nonparametric density estimation for stochastic optimization with an observable state variable. Adv. Neural Inform. Processing Systems 23:820–828.Google Scholar
• Hastie T, Tibshirani R, Friedman JH (2009) The Elements of Statistical Learning: Data mining, inference, and prediction (Springer, New York).Google Scholar
• Hu Y, Kallus N, Mao X (2022) Fast rates for contextual linear optimization. Management Sci. 68(6):4236–4245.Google Scholar
• Kleywegt AJ, Shapiro A (2001) Stochastic optimization. Salvendy G, ed. Handbook of Industrial Engineering (John Wiley & Sons, New York), 2625–2649.Google Scholar
• Lewbel A (2007) A local generalized method of moments estimator. Econom. Lett. 94(1):124–128.Crossref, Google Scholar
• Loubes JM, Marteau C, Solís M (2020) Rates of convergence in conditional covariance matrix with nonparametric entries estimation. Comm. Statist. Theory Methods 49(18):4536–4558.Crossref, Google Scholar
• Meinshausen N, Ridgeway G (2006) Quantile regression forests. J. Machine Learn. Res. 7(6):983–999.Google Scholar
• Mentch L, Hooker G (2016) Quantifying uncertainty in random forests via confidence intervals and hypothesis tests. J. Machine Learn. Res. 17(1):841–881.Google Scholar
• Nemirovski A, Juditsky A, Lan G, Shapiro A (2009) Robust stochastic approximation approach to stochastic programming. SIAM J. Optim. 19(4):1574–1609.Crossref, Google Scholar
• Notz PM (2020) Explainable subgradient tree boosting for prescriptive analytics in operations management. Preprint, submitted August 4, https://dx.doi.org/10.2139/ssrn.3567665.Google Scholar
• Oprescu M, Syrgkanis V, Wu ZS (2019) Orthogonal random forest for causal inference. Chaudhuri K, Salakhutdinov R, eds. Proc. 36th Internat. Conf. on Machine Learn., vol. 97 of Proceedings of Machine Learning Research (PMLR, Long Beach, CA), 4932–4941.Google Scholar
• Rockafellar RT, Uryasev S (2000) Optimization of conditional value-at-risk. J. Risk 2(3):21–42.Crossref, Google Scholar
• Scornet E (2016) Random forests and kernel methods. IEEE Trans. Inform. Theory 62(3):1485–1500.Google Scholar
• Scornet E, Biau G, Vert JP (2016) Consistency of random forests. Ann. Statist. 43(4):1716–1741.Google Scholar
• Shapiro A, Dentcheva D, Ruszczyński A (2014) Lectures on Stochastic Programming: Modeling and Theory (SIAM, Philadelphia).Crossref, Google Scholar
• Simchi-Levi D, Chen X, Bramel J (2005) The logic of logistics. Theory, Algorithms, and Applications for Logistics and Supply Chain Management (Springer, New York).Google Scholar
• Talluri KT, Van Ryzin GJ (2006) The Theory and Practice of Revenue Management (Springer, New York).Google Scholar
• Tibshirani R, Hastie T (1987) Local likelihood estimation. J. Amer. Statist. Assoc. 82(398):559–567.Crossref, Google Scholar
• Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J. Amer. Statist. Assoc. 113(523):1228–1242.Crossref, Google Scholar
• Wilder B, Dilkina B, Tambe M (2019) Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimization. Proc. AAAI Conf. on Artificial Intelligence, 1658–1665.Google Scholar
• Yin J, Geng Z, Li R, Wang H (2010) Nonparametric covariance model. Statist. Sinica 20:469–479.Google Scholar