【RLChina 2024】 专题报告 李帅 Combinatorial Multivariant Multi-Armed Bandits with Appli 32:51 【RLChina 2024】 专题报告 李闽溟 Fairness in Facility Location Games 35:57 【RLChina 2024】 专题报告 李博 MMS Allocation of Indivisible Chores with Subadditive Val 44:29 【RLChina 2024】 专题报告 孔...
Proceedings of the 41st International Conference on Machine Learning (ICML) | July 2024 下载BibTex We introduce a novel framework of combinatorial multi-armed bandits (CMAB) with multivariant and probabilistically triggering arms (CMABMT), where the out...
We investigate the combinatorial multi-armed bandit problem where an action is to select k arms from a set of base arms, and its reward is the maximum of the sample values of these k arms, under a weak feedback structure that only returns the value and...
Yuan, "Combinatorial multi-armed bandit: General framework and applications," in Proceedings of The 30th International Conference on Machine Learning, 2013, pp. 151-159.W. Chen, Y. Wang, and Y. Yuan, "Combinatorial multi-armed bandit: General framework and applications," in Proc. 30th Intl....
Combinatorial network optimization with unknown variables: Multi-armed bandits with linear rewards and individual ob- servations. IEEE/ACM Transactions on ... Y Gai,B Krishnamachari,R Jain - 《IEEE Acm Transactions on Networking》 被引量: 0发表: 2012年 Combinatorial Multi-Armed Bandit and Its Ex...
Top-$k$ Combinatorial Bandits generalize multi-armed bandits, where at each round any subset of $k$ out of $n$ arms may be chosen and the sum of the rewards is gained. We address the full-bandit feedback, in which the agent observes only the sum of rewards, in contrast to the semi...
Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints The problem of multi-armed bandit (MAB) with fairness constraint has emerged as an important research topic recently. For such problems, one common objective is to maximize the total rewards within a fixed round of pulls...
2022微软亚洲研究院数据驱动的优化方法研讨会 报告四:Heavy-Tailed Multi-Armed Bandits 30:03 2022微软亚洲研究院数据驱动的优化方法研讨会 报告二:Efficient Machine Learning at the Edge in Parallel 31:52 2022微软亚洲研究院数据驱动的优化方法研讨会 报告十一:Oblivious Online Contention Resolution Schemes 30...
I want to write my thoughts of the paper [Combinatorial Bandits] by Nicolo Cesa-Bianchi and Gabor Lugosi in 2011. The first author is a great professor in this area. His paper of [Finite-time analysis of the multi-armed bandit problem] in 2002 as second author has been cited over 1,50...
The overlap between reinforcement learning and causality has been recently explored in the simple setting of multi-armed bandits, where an agent’s actions do not affect the state of the environment. By assuming that actions correspond to interventions in a known causal graph, the effects of ...