Results 21 to 30 of about 492,986 (210)
CoRR, 2020 We study a novel variant of the multi-armed bandit problem, where at each time step, the player observes an independently sampled context that determines the arms' mean rewards. However, playing an arm blocks it (across all contexts) for a fixed and known number of future time steps. The above contextual setting, which captures important scenarios such
Soumya Basu 0001 +3 more
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Bayesian Contextual Bandits for Hyper Parameter Optimization
IEEE Access, 2020 Hyper parameter optimization (HPO) is a crucial step in modern machine learning systems. Bayesian optimization (BO) has shown great promise in HPO, where the parameter evaluation is conducted through a black-box optimization procedure.
Guoxin Sui, Yong Yu
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CoRR, 2023 Contextual bandit algorithms are at the core of many applications, including recommender systems, clinical trials, and optimal portfolio selection. One of the most popular problems studied in the contextual bandit literature is to maximize the sum of the rewards in each round by ensuring a sublinear regret against the best-fixed context-dependent ...
Siddhant Chaudhary, Abhishek Sinha
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A Systematic Study on Reinforcement Learning Based Applications
Energies, 2023 We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems ...
Keerthana Sivamayil +5 more
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Sampling the Variational Posterior with Local Refinement
Entropy, 2021 Variational inference is an optimization-based method for approximating the posterior distribution of the parameters in Bayesian probabilistic models. A key challenge of variational inference is to approximate the posterior with a distribution that is ...
Marton Havasi +4 more
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CoRR, 2015 We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context.
Miroslav Dudík +4 more
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Regularized Contextual Bandits
CoRR, 2018 AISTATS 2019, 23 pages, 2 ...
Xavier Fontaine +2 more
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Robust Contextual Linear Bandits
CoRR, 2022 Model misspecification is a major consideration in applications of statistical methods and machine learning. However, it is often neglected in contextual bandits. This paper studies a common form of misspecification, an inter-arm heterogeneity that is not captured by context.
Rong Zhu, Branislav Kveton
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Multi-Agent Contextual Bandits for Multi-Objective Recommendation Systems [PDF]
ITM Web of ConferencesIn this work, the use of multi-agent contextual bandits to optimize multi-objective recommendation systems in resource-constrained settings is examined.
Zhu Ziwen
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Context-aware Multi-stakeholder Recommender Systems
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Traditional recommender systems help users find the most relevant products or services to match their needs and preferences. However, they overlook the preferences of other sides of the market (aka stakeholders) involved in the system.
Tahereh Arabghalizi +1 more
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