Results 11 to 20 of about 3,144 (205)
Multi-class boosting for the analysis of multiple incomplete views on microbiome data. [PDF]
BMC BioinformaticsBackground Microbiome dysbiosis has recently been associated with different diseases and disorders. In this context, machine learning (ML) approaches can be useful either to identify new patterns or learn predictive models.
Simeon A +5 more
europepmc +2 more sources
Thompson Sampling for Non-Stationary Bandit Problems. [PDF]
Entropy (Basel)Non-stationary multi-armed bandit (MAB) problems have recently attracted extensive attention. We focus on the abruptly changing scenario where reward distributions remain constant for a certain period and change at unknown time steps.
Qi H, Guo F, Zhu L.
europepmc +2 more sources
Mating with Multi-Armed Bandits: Reinforcement Learning Models of Human Mate Search. [PDF]
Open Mind (Camb)Conroy-Beam D.
europepmc +2 more sources
Ballooning Multi-Armed Bandits [PDF]
International Joint Conference on Autonomous Agents and Multiagent Systems, 2020 We introduce ballooning multi-armed bandits (BL-MAB), a novel extension to the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. The regret in a BL-MAB setting is computed with respect to the best available arm at each time.
Ganesh Ghalme +4 more
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Multi-Armed Bandits With Correlated Arms [PDF]
IEEE Transactions on Information Theory, 2021 We consider a multi-armed bandit framework where the rewards obtained by pulling different arms are correlated. We develop a unified approach to leverage these reward correlations and present fundamental generalizations of classic bandit algorithms to the correlated setting.
Samarth Gupta +3 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated learning (FL) framework in supervised learning. It is inspired by practical applications in cognitive radio and recommender systems, and enjoys features that are analogous to FL.
Chengshuai Shi, Cong Shen 0001
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Modelling Cournot Games as Multi-agent Multi-armed Bandits
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 We investigate the use of a multi-agent multi-armed bandit (MA-MAB) setting for modeling repeated Cournot oligopoly games, where the firms acting as agents choose from the set of arms representing production quantity (a discrete value).
Kshitija Taywade +3 more
doaj +1 more source
The multi-armed bandit, with constraints [PDF]
Annals of Operations Research, 2012 The colorfully-named and much-studied multi-armed bandit is the following Markov decision problem: At epochs 1, 2, ... , a decision maker observes the current state of each of several Markov chains with rewards (bandits) and plays one of them. The Markov chains that are not played remain in their current states.
Eric V. Denardo +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 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
doaj +1 more source