Parallel Adaptive Survivor Selection
References
- (2010) Finding feasible systems in the presence of constraints on multiple performance measures. ACM Trans. Modeling Comput. Simulation 20(3):13.Crossref, Google Scholar
- (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Royal Statist. Soc. B 57(1):289–300.Crossref, Google Scholar
- (2009) Pure exploration in multi-armed bandits problems. Proc. Internat. Conf. on Algorithmic Learn. Theory (Springer, Berlin), 23–37.Google Scholar
- (2014) Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization. J. Machine Learn. Res. 15:3873–3923.Google Scholar
- (2018) Guarantees on the probability of good selection. Proc. Winter Simulation Conf. (IEEE, New York), 351–365.Google Scholar
- (2012) Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction, vol. 1 (Cambridge University Press, Cambridge, UK).Google Scholar
- (2018) BOHB: Robust and efficient hyperparameter optimization at scale. Proc. Internat. Conf. on Machine Learn. (PMLR), 1437–1446.Google Scholar
- (2016) Indifference-zone-free selection of the best. Oper. Res. 64(6):1499–1514.Link, Google Scholar
- (2010) Decision-theoretic foundations of simulation optimization. Wiley Encyclopedia of Operations Research and Management Sciences (Wiley, New York).Google Scholar
- (2002) Optimization for simulation: Theory vs. practice. INFORMS J. Comput. 14(3):192–215.Link, Google Scholar
- (1979) Bandit processes and dynamic allocation indices. J. Royal Statist. Soc. B 41(2):148–164.Crossref, Google Scholar
- (1990). Batch-size effects on simulation optimization using multiple comparisons with the best. Proc. 1990 Winter Simulation Conf. (IEEE, New York), 288–293.Google Scholar
- (2011) Distilled sensing: Adaptive sampling for sparse detection and estimation. IEEE Trans. Inform. Theory 57(9):6222–6235.Crossref, Google Scholar
- (1988) Discrete event simulations and parallel processing: Statistical properties. SIAM J. Sci. Statist. Comput. 9(6):1114–1132.Crossref, Google Scholar
- Hillel E, Karnin ZS, Koren T, Lempel R, Somekh O (2013) Distributed exploration in multi-armed bandits. Advances in Neural Information Processing Systems, vol. 26 (NIPS, Lake Tahoe, NV).Google Scholar
- (2018) Condition-based maintenance policy optimization using genetic algorithms and Gaussian Markov improvement algorithm. Bregon A, Orchard M, eds. Proc. Annual Conf. of the PHM Soc., vol. 10 (PHM Society, Philadelphia). https://papers.phmsociety.org/index.php/phmconf/issue/view/phm2018 and https://papers.phmsociety.org/index.php/phmconf/article/view/537.Google Scholar
- (2017) Parallel ranking and selection. Tolk A, Fowler J, Shao G, Yücesan E, eds. Advances in Modeling and Simulation (Springer, New York), 249–275.Crossref, Google Scholar
- (2018) A bandit approach to sequential experimental design with false discovery control. Advances in Neural Information Processing Systems, vol. 31 (NIPS, Montreal).Google Scholar
- (2014) Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting. Proc. 48th Annual Conf. on Inform. Sci. and Systems (IEEE, New York), 1–6.Google Scholar
- (2016) Non-stochastic best arm identification and hyperparameter optimization. Artificial Intelligence and Statistics (PMLR), 240–248.Google Scholar
- (2017) Asynchronous parallel Bayesian optimisation via Thompson sampling. Preprint, submitted May 25, https://arxiv.org/abs/1705.09236.Google Scholar
- (2013). Almost optimal exploration in multi-armed bandits. Proc. Internat. Conf. on Machine Learn. (PMLR), 1238–1246.Google Scholar
- (2005) Comparison with a standard via fully sequential procedures. ACM Trans. Modeling Comput. Simulation 15(2):155–174.Crossref, Google Scholar
- (2006a) On the asymptotic validity of fully sequential selection procedures for steady-state simulation. Oper. Res. 54(3):475–488.Link, Google Scholar
- (2006b) Selecting the best system. Henderson S, Nelson BL, eds. Handbooks in Operations Research and Management Science (Elsevier, New York), 501–534.Google Scholar
- (1978) An application of PERT to R&D scheduling. Inform. Processing Management 14(2):121–131.Crossref, Google Scholar
- (2002) An object-oriented random-number package with many long streams and substreams. Oper. Res. 50(6):1073–1075.Link, Google Scholar
- (2017) Hyperband: A novel bandit-based approach to hyperparameter optimization. J. Machine Learn. Res. 18(1):6765–6816.Google Scholar
- (2015) Fully sequential procedures for large-scale ranking-and-selection problems in parallel computing environments. Oper. Res. 63(5):1177–1194.Link, Google Scholar
- (2001) Comparisons with a standard in simulation experiments. Management Sci. 47(3):449–463.Link, Google Scholar
- (2021) Foundations and Methods of Stochastic Simulation: A First Course, 2nd ed. (Springer, New York).Crossref, Google Scholar
- (2001) Simple procedures for selecting the best simulated system when the number of alternatives is large. Oper. Res. 49(6):950–963.Link, Google Scholar
- (2017) Efficient ranking and selection in parallel computing environments. Oper. Res. 65(3):821–836.Link, Google Scholar
- (2013) Simulation optimization: A concise overview and implementation guide. INFORMS Tutorials in Operations Research (INFORMS), 122–150.Link, Google Scholar
- (2018) A new framework for parallel ranking & selection using an adaptive standard. Proc. Winter Simulation Conf. (IEEE), 2201–2212.Google Scholar
- (2020) Evaluation of bi-PASS for parallel simulation optimization. Proc. Winter Simulation Conf. (IEEE), 2960–2971.Google Scholar
- (1992) Adventures in Stochastic Processes (Springer, New York).Google Scholar
- (2020) Simple Bayesian algorithms for best-arm identification. Oper. Res. 68(6):1625–1647.Link, Google Scholar
- (2022) Satisficing in time-sensitive bandit learning. Math. Oper. Res., ePub ahead of print March 14, https://doi.org/10.1287/moor.2021.1229.Google Scholar
- (2019) Gaussian Markov random fields for discrete optimization via simulation: Framework and algorithms. Oper. Res. 67(1):250–266.Link, Google Scholar
- (2021) Rapid discrete optimization via simulation with Gaussian Markov random fields. INFORMS J. Comput. 33(3):915–930.Link, Google Scholar
- (2016) Multiple comparisons with a standard using false discovery rates. Proc. Winter Simulation Conf. (IEEE), 501–511.Google Scholar
- (2008) A new perspective on feasibility determination. Proc. Winter Simulation Conf. (IEEE), 273–280.Google Scholar
- (2015) Multi-armed bandit models for the optimal design of clinical trials: Benefits and challenges. Statist. Sci. 30(2):199.Crossref, Google Scholar
- (1988) Restless bandits: Activity allocation in a changing world. J. Appl. Probability 25:287–298.Crossref, Google Scholar
- (2013) Sequential Bayes-optimal policies for multiple comparisons with a known standard. Oper. Res. 61(5):1174–1189.Link, Google Scholar
- (2022) Knockout-tournament procedures for large-scale ranking and selection in parallel computing environments. Oper. Res. 70(1):432–453.Link, Google Scholar
- (2017). Asynchronous parallel empirical variance guided algorithms for the thresholding bandit problem. Preprint, submitted April 15, https://arxiv.org/abs/1704.04567.Google Scholar

