Balancing Optimal Large Deviations in Sequential Selection
References
- (2012) Analysis of Thompson sampling for the multi-armed bandit problem. Mannor S, Srebro N, Williamson RC, eds. Proc. 2012 Conf. Learn. Theory (PMLR, New York), 39:1–39:26.Google Scholar
- (2010) Fully sequential procedures for comparing constrained systems via simulation. Naval Res. Logist. 57(5):403–421.Crossref, Google Scholar
- (2010) Best arm identification in multi-armed bandits. Kalai AT, Mohri M, eds. Proc. 2010 Conf. Learn. Theory (Omnipress, Madison, WI), 41–53.Google Scholar
- (2003) A framework for simulation-optimization software. IIE Trans. 35(3):221–229.Crossref, Google Scholar
- (2009) Pure exploration in multi-armed bandits problems. Gavaldà R, Lugosi G, Zeugmann T, Zilles S, eds. Proc. 2009 Internat. Conf. Algorithmic Learn. Theory (Springer, Berlin), 23–37.Google Scholar
- (2010) Stochastic Simulation Optimization: An Optimal Computing Budget Allocation (World Scientific, Singapore).Crossref, Google Scholar
- (2017) Rate-optimality of the complete expected improvement criterion. Chan WKV, D’Ambrogio A, Zacharewicz G, Mustafee N, Wainer G, Page E, eds. Proc. 2017 Winter Simulation Conf. (IEEE, Piscataway, NJ), 2173–2182.Google Scholar
- (2019a) Balancing optimal large deviations in ranking and selection. Mustafee N, Bae KH, Lazarova-Molnar S, Rabe M, Szabo C, Haas P, Son YJ, eds. Proc. 2019 Winter Simulation Conf. (IEEE, Piscataway NJ), 3368–3379.Google Scholar
- (2019b) Complete expected improvement converges to an optimal budget allocation. Adv. Appl. Probab. 51(1):209–235.Crossref, Google Scholar
- (2015) Ranking and selection: Efficient simulation budget allocation. Fu MC, ed. Handbook of Simulation Optimization (Springer, New York), 45–80.Crossref, Google Scholar
- (2010) Sequential sampling to myopically maximize the expected value of information. INFORMS J. Comput. 22(1):71–80.Link, Google Scholar
- (2009) Large Deviations Techniques and Applications, 2nd ed. (Springer-Verlag, New York).Google Scholar
- (2016) Indifference-zone-free selection of the best. Oper. Res. 64(6):1499–1514.Link, Google Scholar
- (2016) Optimal computing budget allocation with exponential underlying distribution. Roeder TMK, Frazier PI, Szechtman R, Zhou E, Huschka T, Chick SE, eds. Proc. 2016 Winter Simulation Conf. (IEEE, Piscataway, NJ), 682–689.Google Scholar
- (2017) A new budget allocation framework for the expected opportunity cost. Oper. Res. 65(3):787–803.Link, Google Scholar
- (2016) Optimal best arm identification with fixed confidence. Feldman V, Rakhlin A, Shamir O, eds. Proc. 2016 Conf. Learn. Theory, vol. 49. (PMLR, New York), 998–1027.Google Scholar
- (2004) A large deviations perspective on ordinal optimization. Ingalls RG, Rossetti MD, Smith JS, Peters BA, eds. Proc. 2004 Winter Simulation Conf. (IEEE, Piscataway, NJ), 577–585.Google Scholar
- (2011) Ordinal optimization: A nonparametric framework. Jain S, Creasey RR, Himmelspach J, White KP, Fu MC, eds. Proc. 2011 Winter Simulation Conf. (IEEE, Piscataway, NJ), 4062–4069.Google Scholar
- (2018) Selecting the best system and multi-armed bandits. Preprint, submitted September 10, https://doi.org/10.48550/arXiv.1507.04564.Google Scholar
- (2015) Optimal sampling laws for bi-objective simulation optimization on finite sets. Yilmaz L, Chan WKV, Moon I, Roeder TMK, Macal C, Rossetti MD, eds. Proc. 2015 Winter Simulation Conf. (IEEE, Piscataway, NJ), 3749–3757.Google Scholar
- (2016) Maximizing quantitative traits in the mating design problem via simulation-based Pareto estimation. IIE Trans. 48(6):565–578.Crossref, Google Scholar
- (1998) Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4):455–492.Crossref, Google Scholar
- (2001) A fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Model. Comput. Simulation 11(3):251–273.Google Scholar
- (2006) On the asymptotic validity of fully sequential selection procedures for steady-state simulation. Oper. Res. 54(3):475–488.Link, Google Scholar
- (2017) An efficient fully sequential selection procedure guaranteeing probably approximately correct selection. Chan WKV, D’Ambrogio A, Zacharewicz G, Mustafee N, Wainer G, Page E, eds. Proc. 2017 Winter Simulation Conf. (IEEE, Piscataway, NJ), 2225–2236.Google Scholar
- (1995) Using common random numbers for indifference-zone selection and multiple comparisons in simulation. Management Sci. 41(12):1935–1945.Link, Google Scholar
- (2014) Stochastically constrained ranking and selection via SCORE. ACM Trans. Model. Comput. Simulation 25(1):1–26.Google Scholar
- (2012) Design of computer experiments: Space filling and beyond. Statist. Comput. 22(3):681–701.Crossref, Google Scholar
- (2008) Gaussian process models for computer experiments with qualitative and quantitative factors. Technometrics 50(3):383–396.Crossref, Google Scholar
- (2017) Improving the expected improvement algorithm. Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 30 (Curran Associates, Red Hook, NY), 5381–5391.Google Scholar
- (2020) Simple Bayesian algorithms for best arm identification. Oper. Res. 68(6):1625–1647.Link, Google Scholar
- (2014) Learning to optimize via posterior sampling. Math. Oper. Res. 39(4):1221–1243.Link, Google Scholar
- (2016) On the convergence rates of expected improvement methods. Oper. Res. 64(6):1515–1528.Link, Google Scholar
- (2018) The local time method for targeting and selection. Oper. Res. 66(5):1406–1422.Link, Google Scholar
- (2011) The value of information in multi-armed bandits with exponentially distributed rewards. Sato M, Matsuoka S, Sloot PM, van Albada GD, Dongarra J, eds. Proc. 2011 Internat. Conf. Computational Sci. (Elsevier, Amsterdam), 1363–1372.Google Scholar
- (2014) Discrete optimization via simulation using Gaussian Markov random fields. Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA, eds. Proc. 2014 Winter Simulation Conf. (IEEE, Piscataway, NJ), 3809–3820.Google Scholar
- (2018) Tractable sampling strategies for ordinal optimization. Oper. Res. 66(6):1693–1712.Link, Google Scholar
- (2018) Analyzing and provably improving fixed budget ranking and selection algorithms. Preprint, submitted November 26, https://doi.org/10.48550/arXiv.1811.12183.Google Scholar
- (2010) Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation. ACM Trans. Model. Comput. Simulation 20(1):3:1–3:29.Google Scholar
- (2013) Designs for crossvalidating approximation models. Biometrika 100(4):997–1004.Crossref, Google Scholar
- (2016) A simulation budget allocation procedure for enhancing the efficiency of optimal subset selection. IEEE Trans. Automatic Control 61(1):62–75.Crossref, Google Scholar

