Results 21 to 30 of about 3,478,919 (201)
Multi-Armed Bandits for Spectrum Allocation in Multi-Agent Channel Bonding WLANs
IEEE Access, 2021 While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied to the primary and secondary channel selection.
Sergio Barrachina-Munoz +2 more
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Be Greedy in Multi-Armed Bandits
CoRR, 2021 The Greedy algorithm is the simplest heuristic in sequential decision problem that carelessly takes the locally optimal choice at each round, disregarding any advantages of exploring and/or information gathering. Theoretically, it is known to sometimes have poor performances, for instance even a linear regret (with respect to the time horizon) in the ...
Matthieu Jedor +2 more
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The Assistive Multi-Armed Bandit [PDF]
2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2019 Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the
Lawrence Chan +3 more
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Multi-armed quantum bandits: Exploration versus exploitation when learning properties of quantum states [PDF]
Quantum, 2022 We initiate the study of tradeoffs between exploration and exploitation in online learning of properties of quantum states. Given sequential oracle access to an unknown quantum state, in each round, we are tasked to choose an observable from a set of ...
Josep Lumbreras +2 more
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Multi-armed bandits with dependent arms
Machine Learning, 2023 We study a variant of the classical multi-armed bandit problem (MABP) which we call as Multi-Armed Bandits with dependent arms. More specifically, multiple arms are grouped together to form a cluster, and the reward distributions of arms belonging to the same cluster are known functions of an unknown parameter that is a characteristic of the cluster ...
Rahul Singh 0001 +3 more
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Multi-Armed Bandits and Quantum Channel Oracles [PDF]
QuantumMulti-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and it was found that a quadratic speed-up (in query complexity) is possible ...
Simon Buchholz +2 more
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On Kernelized Multi-armed Bandits
CoRR, 2017 We consider the stochastic bandit problem with a continuous set of arms, with the expected reward function over the arms assumed to be fixed but unknown. We provide two new Gaussian process-based algorithms for continuous bandit optimization-Improved GP-UCB (IGP-UCB) and GP-Thomson sampling (GP-TS), and derive corresponding regret bounds. Specifically,
Sayak Ray Chowdhury, Aditya Gopalan
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Satisficing in Multi-Armed Bandit Problems [PDF]
IEEE Transactions on Automatic Control, 2017 To appear in IEEE Transactions on Automatic ...
Paul Reverdy +2 more
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Multi-armed bandits for performance marketing
International Journal of Data Science and Analytics, 2023 Abstract This paper deals with the problem of optimising bids and budgets of a set of digital advertising campaigns. We improve on the current state of the art by introducing support for multi-ad group marketing campaigns and developing a highly data efficient parametric contextual bandit.
Gigli, M, Stella, F
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QoS-Aware Multi-armed Bandits [PDF]
2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W), 2016 Motivated by runtime verification of QoS requirements in self-adaptive and self-organizing systems that are able to reconfigure their structure and behavior in response to runtime data, we propose a QoS-aware variant of Thompson sampling for multi-armed bandits.
Lenz Belzner, Thomas Gabor
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