Results 11 to 20 of about 4,126,170 (169)
Contextual Multi-Armed Bandits for Non-Stationary Wireless Network Selection [PDF]
GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023 This work has been funded through the project ROUTE56 (Agencia Estatal de Investigación, Ministerio de Ciencia e Innovación, PID2019-104945GB-I00/ AEI / 10.13039/501100011033) and the grants 22CO1/008248 and 2021 SGR 01033 (AGAUR, Generalitat de Catalunya).
Martínez Casanovas, Lluís +2 more
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Local Clustering in Contextual Multi-Armed Bandits [PDF]
Proceedings of the Web Conference 2021, 2021 We study identifying user clusters in contextual multi-armed bandits (MAB). Contextual MAB is an effective tool for many real applications, such as content recommendation and online advertisement. In practice, user dependency plays an essential role in the user's actions, and thus the rewards.
Yikun Ban, Jingrui He
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A generalised gittins index for a class of multi-armed bandits with general resource requirements [PDF]
, 2009 We generalise classical multiarmed bandits to allow for the distribution of a (fixed amount of a) divisible resource among the constituent bandits at each decision point.
Minty, R J, Glazebrook, K D
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Adaptive Endpointing with Deep Contextual Multi-Armed Bandits
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Current endpointing (EP) solutions learn in a supervised framework, which does not allow the model to incorporate feedback and improve in an online setting. Also, it is a common practice to utilize costly grid-search to find the best configuration for an endpointing model.
Do June Min +6 more
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With a little help from my 'ordinary friends': relationships, networks, and resilience in Masisi, North Kivu, eastern Democratic Republic of the Congo. [PDF]
DisastersAbstract Are social networks the key to understanding resilience in conflict? Recent studies suggest so, but relational research in conflict‐affected areas is rare. What exists stresses the importance of small circles of close family members, trusted friends, and co‐ethnic persons/groups, but tends to overlook their aggregate effect.
Fontana SG.
europepmc +2 more sources
MAB-OS: Multi-Armed Bandits Metaheuristic Optimizer Selection
, 2022 Metaheuristic algorithms are derivative-free optimizers designed to estimate the global optima for optimization problems. Keeping balance between exploitation and exploration and the performance complementarity between the algorithms have led to the ...
Meidani, Kazem +5 more
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Deep Contextual Multi-armed Bandits
CoRR, 2018 Contextual multi-armed bandit problems arise frequently in important industrial applications. Existing solutions model the context either linearly, which enables uncertainty driven (principled) exploration, or non-linearly, by using epsilon-greedy exploration policies. Here we present a deep learning framework for contextual multi-armed bandits that is
Mark Collier, Hector Urdiales Llorens
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Local Clustering in Contextual Multi-Armed Bandits [PDF]
, 2023 We study identifying user clusters in contextual multi-armed bandits (MAB). Contextual MAB is an effective tool for many real applications, such as content recommendation and online advertisement.
He, Jingrui, Ban, Yikun
core +1 more source
Contextual multi-armed bandits for web server defense [PDF]
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012 In this paper we argue that contextual multi-armed bandit algorithms could open avenues for designing self-learning security modules for computer networks and related tasks. The paper has two contributions: a conceptual and an algorithmical one. The conceptual contribution is to formulate the real-world problem of preventing HTTP-based attacks on web ...
Tobias Jung +3 more
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Dynamic Clustering of Contextual Multi-Armed Bandits
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014 With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge.
Trong T. Nguyen, Hady Wirawan Lauw
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