Articles with public access mandates - David EckmanLearn more
Available somewhere: 20
Guarantees on the probability of good selection
DJ Eckman, SG Henderson
2018 Winter Simulation Conference (WSC), 351-365, 2018
Mandates: US National Science Foundation, US Department of Defense
Reusing search data in ranking and selection: What could possibly go wrong?
DJ Eckman, SG Henderson
ACM Transactions on Modeling and Computer Simulation (TOMACS) 28 (3), 1-15, 2018
Mandates: US National Science Foundation
Diagnostic tools for evaluating and comparing simulation-optimization algorithms
DJ Eckman, SG Henderson, S Shashaani
INFORMS Journal on Computing 35 (2), 350-367, 2023
Mandates: US National Science Foundation
SimOpt: A testbed for simulation-optimization experiments
DJ Eckman, SG Henderson, S Shashaani
INFORMS Journal on Computing 35 (2), 495-508, 2023
Mandates: US National Science Foundation
Empirically comparing the finite-time performance of simulation-optimization algorithms
NA Dong, DJ Eckman, X Zhao, SG Henderson, M Poloczek
2017 Winter Simulation Conference (WSC), 2206-2217, 2017
Mandates: US National Science Foundation, US Department of Defense
Plausible screening using functional properties for simulations with large solution spaces
DJ Eckman, M Plumlee, BL Nelson
Operations Research 70 (6), 3473-3489, 2022
Mandates: US National Science Foundation
Posterior-based stopping rules for Bayesian ranking-and-selection procedures
DJ Eckman, SG Henderson
INFORMS Journal on Computing 34 (3), 1711-1728, 2022
Mandates: US National Science Foundation, US Department of Defense
Revisiting subset selection
DJ Eckman, M Plumlee, BL Nelson
2020 Winter Simulation Conference (WSC), 2972-2983, 2020
Mandates: US National Science Foundation
Biased gradient estimators in simulation optimization
DJ Eckman, SG Henderson
2020 Winter Simulation Conference (WSC), 2935-2946, 2020
Mandates: US National Science Foundation, US Department of Defense
Fixed-confidence, fixed-tolerance guarantees for selection-of-the-best procedures
DJ Eckman, SG Henderson
Technical report, Working paper, Cornell University, School of Operations …, 2018
Mandates: US National Science Foundation, US Department of Defense
Redesigning a testbed of simulation-optimization problems and solvers for experimental comparisons
DJ Eckman, SG Henderson, R Pasupathy
2019 Winter Simulation Conference (WSC), 3457-3467, 2019
Mandates: US National Science Foundation, US Department of Defense
Green simulation optimization using likelihood ratio estimators
DJ Eckman, MB Feng
2018 Winter Simulation Conference (WSC), 2049-2060, 2018
Mandates: US National Science Foundation
Sensitivity analysis of an ICU simulation model
T Bountourelis, D Eckman, L Luangkesorn, A Schaefer, SG Nabors, ...
Proceedings of the 2012 Winter Simulation Conference (WSC), 1-12, 2012
Mandates: US National Institutes of Health
Fixed-confidence, fixed-tolerance guarantees for ranking-and-selection procedures
DJ Eckman, SG Henderson
ACM Transactions on Modeling and Computer Simulation (TOMACS) 31 (2), 1-33, 2021
Mandates: US National Science Foundation, US Department of Defense
Optimal pinging frequencies in the search for an immobile beacon
DJ Eckman, LM Maillart, AJ Schaefer
IIE Transactions 48 (6), 489-500, 2016
Mandates: US National Science Foundation
Evaluating and comparing simulation-optimization algorithms
DJ Eckman, SG Henderson, S Shashaani
Under review, 2021
Mandates: US National Science Foundation
Automatic differentiation for gradient estimators in simulation
MT Ford, SG Henderson, DJ Eckman
2022 Winter Simulation Conference (WSC), 3134-3145, 2022
Mandates: US National Science Foundation
Flat chance! using stochastic gradient estimators to assess plausible optimality for convex functions
DJ Eckman, M Plumlee, BL Nelson
2021 Winter Simulation Conference (WSC), 1-12, 2021
Mandates: US National Science Foundation
Comparing Frequentist and Bayesian Fixed-Confidence Guarantees for Selection-of-the-Best Problems
D Eckman, S Henderson
Mandates: US National Science Foundation, US Department of Defense
Probably Approximately Correct (PAC) Selection in Simulation/Best-Arm Problems
D Eckman, S Henderson
Mandates: US National Science Foundation
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