【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 define a general framework for a large class of combinatorial multi-armed bandit (CMAB) problems, where simple arms with unknown distributions form super arms. In each round, a super arm is played and the outcomes of its related simple arms are observed, which helps th...
Search algorithms based on combinatorial multi-armed bandits (CMABs) are promising for dealing with state-space sequential decision problems. However, current CMAB-based algorithms do not scale to problem domains with very large actions spaces, such as real-time strategy games played in large maps...
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...
In this paper we study a generalized version of classical multi-armed bandits (MABs) problem by allowing for arbitrary constraints on constituent bandits at each decision point. The motivation of this study comes from many situations that involve repeatedly making choices subject to arbitrary ...
Prior work on multi-armed bandits with multiple plays cannot be applied to this formulation because of the general nature of the constraint. On the other hand, the mapping of all feasible combinations to arms allows for the use of prior work on MAB with single-play, but results in regret,...
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...
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...
To address these problems, we propose an algorithm named Dynamic Clustering based Contextual Combinatorial Multi-Armed Bandits (DC3MAB), which consists of three configurable key components. Specifically, a dynamic user clustering strategy enables different users in the same cluster to cooperate in ...