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Conference and Workshop Papers
- 2023
[c9]Ziyi Huang, Henry Lam, Amirhossein Meisami, Haofeng Zhang:
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework. NeurIPS 2023- 2022
- [c8]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Efficient Reinforcement Learning with Prior Causal Knowledge. CLeaR 2022: 526-541 - 2021
- [c7]Ziping Xu, Amirhossein Meisami, Ambuj Tewari:
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns. AISTATS 2021: 127-135 - [c6]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Low-Rank Generalized Linear Bandit Problems. AISTATS 2021: 460-468 - [c5]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Causal Bandits with Unknown Graph Structure. NeurIPS 2021: 24817-24828 - 2020
- [c4]Haoxian Chen, Henry Lam, Fengpei Li, Amirhossein Meisami:
Constrained Reinforcement Learning via Policy Splitting. ACML 2020: 209-224 - [c3]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, William Yan:
Regret Analysis of Bandit Problems with Causal Background Knowledge. UAI 2020: 141-150 - 2018
- [c2]Amirhossein Meisami, Henry Lam, Chen Dong, Abhishek Pani:
Sequential Learning under Probabilistic Constraints. UAI 2018: 621-631 - 2017
- [c1]Amirhossein Meisami, Mark P. Van Oyen, Henry Lam:
Uncertainty quantification on simulation analysis driven by random forests. WSC 2017: 3266-3274
Parts in Books or Collections
- 2014
- [p1]Krishna Reddy Gujjula, Krishnan Ayalur Seshadrinathan, Amirhossein Meisami:
A Hybrid Metaheuristic for the Maximum k-Plex Problem. Examining Robustness and Vulnerability of Networked Systems 2014: 83-92
Informal and Other Publications
- 2023
- [i7]Jun Tao, Qian Chen, James W. Snyder Jr., Arava Sai Kumar, Amirhossein Meisami, Lingzhou Xue:
A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution. CoRR abs/2302.06075 (2023) - 2022
- [i6]Ziyi Huang, Henry Lam, Amirhossein Meisami, Haofeng Zhang:
Generalized Bayesian Upper Confidence Bound with Approximate Inference for Bandit Problems. CoRR abs/2201.12955 (2022) - 2021
- [i5]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Causal Markov Decision Processes: Learning Good Interventions Efficiently. CoRR abs/2102.07663 (2021) - [i4]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Causal Bandits with Unknown Graph Structure. CoRR abs/2106.02988 (2021) - 2020
- [i3]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Low-Rank Generalized Linear Bandit Problems. CoRR abs/2006.02948 (2020) - [i2]Ziping Xu, Amir Meisami, Ambuj Tewari:
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns. CoRR abs/2010.08048 (2020) - 2019
- [i1]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, Zhenyu Yan:
Regret Analysis of Causal Bandit Problems. CoRR abs/1910.04938 (2019)
Coauthor Index
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