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The Fair Contextual Multi-Armed Bandit
International Joint Conference on Autonomous Agents and Multiagent Systems, 2020When an AI system interacts with multiple users, it frequently needs to make allocation decisions. For instance, a virtual agent decides whom to pay attention to in a group setting, or a factory robot selects a worker to deliver a part. Demonstrating fairness in decision making is essential for such systems to be broadly accepted.
Yifang Chen 0004 +5 more
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Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits [PDF]
IEEE Control Systems Letters, 2022 Contextual multi-armed bandits are classical models in reinforcement learning for sequential decision-making associated with individual information. A widely-used policy for bandits is Thompson Sampling, where samples from a data-driven probabilistic belief about unknown parameters are used to select the control actions.
Mohamad Kazem Shirani Faradonbeh +1 more
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Context Enhancement for Linear Contextual Multi-Armed Bandits
2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018Contextual Multi-Armed Bandit (CMAB) algorithms such as LinUCB (Linear Upper Confidence Bound) or Contextual Thompson Sampling (CTS) base their resolution on the assumption that there exists a linear dependency between the expected reward of an action and its context.
Gutowski, Nicolas +3 more
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A Contextual Multi-Armed Bandit Approach for NDN Forwarding
Journal of Network and Computer ApplicationsBadis Djamaà
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Dynamic Contextual Multi Arm Bandits in Display Advertisement
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016We model the ad selection task as a multi-armed bandit problem. Standard assumptions in the multi-armed bandit (MAB) setting are that samples drawn from each arm areindependent and identically distributed, rewards (or conversionrates in our scenario) are stationary and rewards feedback areimmediate.
Hongxia Yang, Quan Lu
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MABWiser: A Parallelizable Contextual Multi-Armed Bandit Library for Python
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019Contextual multi-armed bandit algorithms serve as an effective technique to address online sequential decision-making problems. Despite their popularity, when it comes to off-the-shelf tools the library support remains limited, in particular for the Python technology stack. To fill this gap, in this paper we present a system that provides context-free,
Serdar Kadioglu +2 more
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Global versus individual accuracy in contextual multi-armed bandit
Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019Most works on Multi-Armed Bandits focus the evaluations of their methods on a global accuracy performance metric. In the case of Contextual Multi-Armed Bandit (CMAB), the existing algorithms claim to eventually provide full personalization. This suggests that their global accuracy metric should reflect each user's individual accuracy.
Nicolas Gutowski +3 more
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A Survey on Contextual Multi-Armed Bandits
Applied and Computational EngineeringAs a powerful reinforcement learning framework, Contextual Multi-Armed Bandits have extensive applications in various domains. The models of Contextual Multi-Armed Bandits enable decision-makers to make intelligent choices in situations with uncertainty, and they find utility in fields such as online advertising, medical treatment optimization ...
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Uplifting Contextual Multi-armed Bandits
2019Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community. In particular, estimating the individual-treatment-effect (ITE) in settings such as precision medicine and targeted advertising has peaked application.
Berrevoets, Jeroen +2 more
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Contextual Multi-Armed Bandit for Email Layout Recommendation
Proceedings of the 17th ACM Conference on Recommender Systems, 2023Yan Chen 0019 +6 more
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