재정 지원 요구사항을 통해 공개된 자료 - Peter W. Glynn자세히 알아보기
제공된 곳이 없음: 1
Using regenerative simulation to calibrate exponential approximations to risk measures of hitting times to rarely visited sets
PW Glynn, MK Nakayama, B Tuffin
2018 Winter Simulation Conference (WSC), 1802-1813, 2018
재정 지원 요구사항 정책: US National Science Foundation
제공된 곳이 있음: 39
Statistics of robust optimization: A generalized empirical likelihood approach
JC Duchi, PW Glynn, H Namkoong
Mathematics of Operations Research 46 (3), 946-969, 2021
재정 지원 요구사항 정책: US National Science Foundation, US Department of Defense
Likelihood robust optimization for data-driven problems
Z Wang, PW Glynn, Y Ye
Computational Management Science 13, 241-261, 2016
재정 지원 요구사항 정책: US National Science Foundation
Persistence probabilities and exponents
LN Andersen, S Asmussen, F Aurzada, PW Glynn, M Maejima, ...
Lévy Matters V: Functionals of Lévy Processes, 183-224, 2015
재정 지원 요구사항 정책: German Research Foundation
Finite-sample regret bound for distributionally robust offline tabular reinforcement learning
Z Zhou, Z Zhou, Q Bai, L Qiu, J Blanchet, P Glynn
International Conference on Artificial Intelligence and Statistics, 3331-3339, 2021
재정 지원 요구사항 정책: US National Science Foundation, US Department of Defense
The cross-entropy method for estimation
DP Kroese, RY Rubinstein, PW Glynn
Handbook of statistics 31, 19-34, 2013
재정 지원 요구사항 정책: Australian Research Council
On sampling rates in simulation-based recursions
R Pasupathy, P Glynn, S Ghosh, FS Hashemi
SIAM Journal on Optimization 28 (1), 45-73, 2018
재정 지원 요구사항 정책: US National Science Foundation, US Department of Defense
On the convergence of mirror descent beyond stochastic convex programming
Z Zhou, P Mertikopoulos, N Bambos, SP Boyd, PW Glynn
SIAM Journal on Optimization 30 (1), 687-716, 2020
재정 지원 요구사항 정책: Agence Nationale de la Recherche
Adaptive experimental design with temporal interference: A maximum likelihood approach
PW Glynn, R Johari, M Rasouli
Advances in Neural Information Processing Systems 33, 15054-15064, 2020
재정 지원 요구사항 정책: US National Science Foundation
Affine point processes: Approximation and efficient simulation
X Zhang, J Blanchet, K Giesecke, PW Glynn
Mathematics of Operations Research 40 (4), 797-819, 2015
재정 지원 요구사항 정책: Research Grants Council, Hong Kong
Smoothed variable sample-size accelerated proximal methods for nonsmooth stochastic convex programs
A Jalilzadeh, U Shanbhag, J Blanchet, PW Glynn
Stochastic Systems 12 (4), 373-410, 2022
재정 지원 요구사항 정책: US National Science Foundation, US Department of Energy
Countering feedback delays in multi-agent learning
Z Zhou, P Mertikopoulos, N Bambos, PW Glynn, C Tomlin
Advances in Neural Information Processing Systems 30, 2017
재정 지원 요구사항 정책: US National Science Foundation
Probability functional descent: A unifying perspective on GANs, variational inference, and reinforcement learning
C Chu, J Blanchet, P Glynn
International Conference on Machine Learning, 1213-1222, 2019
재정 지원 요구사항 정책: US National Science Foundation
Constructing simulation output intervals under input uncertainty via data sectioning
PW Glynn, H Lam
2018 Winter Simulation Conference (WSC), 1551-1562, 2018
재정 지원 요구사항 정책: US National Science Foundation
Analysis of a stochastic approximation algorithm for computing quasi-stationary distributions
J Blanchet, P Glynn, S Zheng
Advances in Applied Probability 48 (3), 792-811, 2016
재정 지원 요구사항 정책: US National Science Foundation
Robust power management via learning and game design
Z Zhou, P Mertikopoulos, AL Moustakas, N Bambos, P Glynn
Operations Research 69 (1), 331-345, 2021
재정 지원 요구사항 정책: US Department of Defense, European Commission, Agence Nationale de la Recherche
On the marginal standard error rule and the testing of initial transient deletion methods
RJ Wang, PW Glynn
ACM Transactions on Modeling and Computer Simulation (TOMACS) 27 (1), 1-30, 2016
재정 지원 요구사항 정책: Natural Sciences and Engineering Research Council of Canada
Computing Bayesian means using simulation
S Andradóttir, PW Glynn
ACM Transactions on Modeling and Computer Simulation (TOMACS) 26 (2), 1-26, 2016
재정 지원 요구사항 정책: US National Science Foundation
On the asymptotic analysis of quantile sensitivity estimation by Monte Carlo simulation
Y Peng, MC Fu, PW Glynn, J Hu
2017 Winter Simulation Conference (WSC), 2336-2347, 2017
재정 지원 요구사항 정책: US National Science Foundation, US Department of Defense, National Natural …
Computing sensitivities for distortion risk measures
PW Glynn, Y Peng, MC Fu, JQ Hu
INFORMS Journal on Computing 33 (4), 1520-1532, 2021
재정 지원 요구사항 정책: US National Science Foundation, US Department of Defense, National Natural …
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