| 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 | 54 | 2020 |
| Low-rank generalized linear bandit problems Y Lu, A Meisami, A Tewari International Conference on Artificial Intelligence and Statistics, 460-468, 2021 | 49 | 2021 |
| 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 | 41 | 2021 |
| Causal bandits with unknown graph structure Y Lu, A Meisami, A Tewari Advances in Neural Information Processing Systems 34, 24817-24828, 2021 | 34 | 2021 |
| Efficient reinforcement learning with prior causal knowledge Y Lu, A Meisami, A Tewari Conference on Causal Learning and Reasoning, 526-541, 2022 | 18 | 2022 |
| 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 | 14 | 2013 |
| 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 | 12 | 2014 |
| 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 | 9 | 2019 |
| 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 | 7 | 2021 |
| Causal Markov decision processes: Learning good interventions efficiently Y Lu, A Meisami, A Tewari arXiv preprint arXiv:2102.07663, 2021 | 6 | 2021 |
| 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 | 6 | 2012 |
| 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 | 4 | 2022 |
| 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 | 2 | 2010 |
| 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 | 1 | 2022 |
| Constrained Reinforcement Learning via Policy Splitting H Chen, H Lam, F Li, A Meisami Asian Conference on Machine Learning, 209-224, 2020 | 1 | 2020 |
| Integrated machine learning and optimization frameworks with applications in operations management A Meisami | 1 | 2018 |
| Sequential Learning under Probabilistic Constraints. A Meisami, H Lam, C Dong, A Pani UAI, 621-631, 2018 | 1 | 2018 |
| Uncertainty quantification on simulation analysis driven by random forests A Meisami, MP Van Oyen, H Lam 2017 Winter Simulation Conference (WSC), 3266-3274, 2017 | 1 | 2017 |
| 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 |
Ambuj Tewari | अम्बुज तिवारीProfessor, Statistics and EECS, University of MichiganGeverifieerd e-mailadres voor umich.edu
