main webpage W Topic Multi-Armed Bandit arXiv (Cornell University) Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms 2021 The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online advertising and clinical trials. As many other machine learning algorithms, contextual… Article Open Multi-Armed Bandit Open Article Page

Multi-Armed Bandit

Resource problem in machine learning In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K - or N -armed bandit problem ) is named from imagining a gambler at a row of slot machines (sometimes known as "one-armed bandits"), who has to decide which machines to play, how many times to play each machine and in which order to play them, and whether to continue with the current machine or try a different machine. More generally, it is a problem in which a decision maker iteratively selects one of multiple fixed choices (i.e., arms or actions) when the properties of each choice are only partially known at the time of allocation, and may become better understood as time passes. Critical Symbolic Virtual Narrative Open Multi-Armed Bandit Open Article Page

Exploring foci of: arXiv (Cornell University) Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms 2021 The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online advertising and clinical trials. As many other machine learning algorithms, contextual bandit algorithms often have one or more hyper-parameters. As an example, in most optimal stochastic contextual bandit algorithms, there is an unknown exploration parameter which controls the trade-off between exploration and exploitation. A proper choice of … Open Article Page

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Explore Multi-Armed Bandit W Topic Multi-Armed Bandit arXiv (Cornell University) Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms 2021 The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online advertising and clinical trials. As many other machine learning algorithms, contextual… Article Critical Symbolic Virtual Narrative Open Multi-Armed Bandit Open Article Page