multi-armed banditsonline learningworker recruitmentMobile Crowdsensing (MCS) represents a transformative approach to collecting data from the environment as it utilizes the ubiquity and sensory capabilities of mobile devices with human participants. This paradigm enables scales of data collection critical ...
The combinatorial multi-armed bandit (CMAB) is a fundamental sequential decision-making framework, extensively studied over the past decade. However, existing work primarily focuses on the online setting, overlooking the substantial costs of online interactions and the readily available offline datasets....
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...
Two-Stage Client Selection for Federated Learning Against Free-Riding Attack: A Multiarmed Bandits and Auction-Based Approach 2024, IEEE Internet of Things Journal Optimal Time and Energy-Aware Client Selection Algorithms for Federated Learning on Heterogeneous Resources 2024, Proceedings - Symposium on ...
【RLChina 2024】 专题报告 李帅 Combinatorial Multivariant Multi-Armed Bandits with AppliRLChina强化学习社区 立即播放 打开App,流畅又高清100+个相关视频 更多 1249 1 11:13 App 【RLChina论文研讨会】第103期 吕怡琴 Theoretical Investigations and Practical Enhancements on 241 0 51:20 App 【RLChina 2024...
Best Arm Identification in Multi-armed Bandits with Delayed Feedback PMLR, 2018. paper Grover, Aditya and Markov, Todor and Attia, Peter and Jin, Norman and Perkins, Nicolas and Cheong, Bryan and Chen, Michael and Yang, Zi and Harris, Stephen and Chueh, William and others Ranked Reward: ...
Schemata bandits are influenced by two different areas in machine learning, evolutionary computation and multi-armed bandits. The schemata from the schema theorem for genetic algorithms are structured as hierarchical multi-armed bandits in order to focus the optimisation in promising areas of the search...
Karnin, Z., Koren, T., Somekh, O.: Almost optimal exploration in multi-armed bandits. In: Proceedings of the 30th International Conference on Machine Learning, pp. 1238–1246 (2013) Mannor, S., Tsitsiklis, J.N.: The sample complexity of exploration in the multi-armed bandit problem. J...
Best arm identification in multi-armed bandits with delayed feedback Huiling Huiling Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method Xijun Xijun A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem Xijun Xijun Pointer Networks Huiling, Xijun ...
In the classic multi-armed bandits problem, the goal is to have a policy for dynamically operating arms that each yield stochastic rewards with unknown means. The key metric of interest is regret, defined as the gap between the expected total reward accumulated by an omniscient player that ...