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Amirhossein Meisami
Amirhossein Meisami
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Title
Cited by
Cited by
Year
Regret analysis of bandit problems with causal background knowledge
Y Lu, A Meisami, A Tewari, W Yan
Conference on Uncertainty in Artificial Intelligence, 141-150, 2020
542020
Low-rank generalized linear bandit problems
Y Lu, A Meisami, A Tewari
International Conference on Artificial Intelligence and Statistics, 460-468, 2021
472021
Effect of a predictive model on planned surgical duration accuracy, patient wait time, and use of presurgical resources: a randomized clinical trial
CT Strömblad, RG Baxter-King, A Meisami, SJ Yee, MR Levine, ...
JAMA surgery 156 (4), 315-321, 2021
412021
Causal bandits with unknown graph structure
Y Lu, A Meisami, A Tewari
Advances in Neural Information Processing Systems 34, 24817-24828, 2021
342021
Efficient reinforcement learning with prior causal knowledge
Y Lu, A Meisami, A Tewari
Conference on Causal Learning and Reasoning, 526-541, 2022
182022
Emergency relief routing and temporary depots location problem considering roads restoration
SA Torabi, M Baghersad, A Meisami
Proceedings of the 24th annual conference of the production and operations …, 2013
142013
A Hybrid Metaheuristic for the Maximum k-Plex Problem.
KR Gujjula, KA Seshadrinathan, A Meisami
Examining robustness and vulnerability of networked systems 37, 83-92, 2014
122014
Data-driven optimization methodology for admission control in critical care units
A Meisami, J Deglise-Hawkinson, ME Cowen, MP Van Oyen
Health care management science 22, 318-335, 2019
92019
Decision making problems with funnel structure: a multi-task learning approach with application to email marketing campaigns
Z Xu, A Meisami, A Tewari
International Conference on Artificial Intelligence and Statistics, 127-135, 2021
72021
Causal Markov decision processes: Learning good interventions efficiently
Y Lu, A Meisami, A Tewari
arXiv preprint arXiv:2102.07663, 2021
62021
A framework for performance measurement of humanitarian relief chains: a combined fuzzy DEMATEL-ANP approach
SA Torabi, M Aghabegloo, A Meisami
Production and Operations Management Society 1 (1), 1-10, 2012
62012
Quantile regression forests for individualized surgery scheduling
A Dean, A Meisami, H Lam, MP Van Oyen, C Stromblad, N Kastango
Health Care Management Science 25 (4), 682-709, 2022
42022
Information technology acceptance models comparison and IT development strategies: In small and medium sized enterprises case
M Movahedi, M Zamanian, A Meisami
2010 IEEE International Conference on Industrial Engineering and Engineering …, 2010
22010
Generalized Bayesian upper confidence bound with approximate inference for bandit problems
Z Huang, H Lam, A Meisami, H Zhang
arXiv preprint arXiv:2201.12955, 2022
12022
Constrained Reinforcement Learning via Policy Splitting
H Chen, H Lam, F Li, A Meisami
Asian Conference on Machine Learning, 209-224, 2020
12020
Integrated machine learning and optimization frameworks with applications in operations management
A Meisami
12018
Sequential Learning under Probabilistic Constraints.
A Meisami, H Lam, C Dong, A Pani
UAI, 621-631, 2018
12018
Uncertainty quantification on simulation analysis driven by random forests
A Meisami, MP Van Oyen, H Lam
2017 Winter Simulation Conference (WSC), 3266-3274, 2017
12017
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
Z Huang, H Lam, A Meisami, H Zhang
Advances in Neural Information Processing Systems 36, 2024
2024
Optimal Regret Is Achievable With Constant Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
Z Huang, H Lam, A Meisami, H Zhang
arXiv preprint arXiv:2201.12955, 2022
2022
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