Results 121 to 130 of about 12,386 (222)
, 2023 Many real applications of bandits have sparse non-zero rewards, leading to slow learning rates. A careful distribution modeling that utilizes problem-specific structures is known as critical to estimation efficiency in the statistics literature, yet is ...
Wei, Haoyu +3 more
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Czech Journal of International Relations, 2013 John Arquilla: Insurgents, Raiders, and Bandits: How Masters of Irregular Warfare Have Shaped Our World. 1st ed. Lanham: Ivan R. Dee, 2011, 311 stran, ISBN 978-1-56663-832-6.
Petr Kalinič
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The Bandit Whisperer: Communication Learning for Restless Bandits
Proceedings of the AAAI Conference on Artificial IntelligenceApplying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a promising avenue for addressing allocation problems with resource constraints and temporal dynamics. However, classic RMAB models largely overlook the challenges of (systematic) data errors - a common occurrence in real-world scenarios due to factors like varying data ...
Yunfan Zhao +4 more
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, 1987 Bandits assembles an unlikely crew: an ex-cop, an ex-con, and an ex-nun. They've got their eyes on several million bucks intended for the Contras; with their unique set of skills and their crazily clever plan, they're sure to make out like bandits -- if ...
Leonard, Elmore 1925-2013
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Multivariate Bandits and Their Applications [PDF]
, 2010 We will review the multi-armed bandit problem and its application to optimizing click-through for Web site banners. We will present multi-variate extensions to the basic bandit technology including the use of Gaussian Processes to model relations between different arms.
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Cascading Bandits with Feedback
2025 First International Conference on Intelligent Computing and Systems at the Edge (ICEdge)Motivated by the challenges of edge inference, we study a variant of the cascade bandit model in which each arm corresponds to an inference model with an associated accuracy and error probability. We analyse four decision-making policies-Explore-then-Commit, Action Elimination, Lower Confidence Bound (LCB), and Thompson Sampling-and provide sharp ...
Rudrabhotla Sri Prakash +2 more
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MathematicsThe multi-armed bandit (MAB) problem is a foundational model for sequential decision-making under uncertainty. While MAB has proven valuable in applications such as clinical trials and online advertising, traditional formulations have limitations ...
Ali Baheri
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Stochastic k-Submodular Bandits with Full Bandit Feedback
International Joint Conference on Autonomous Agents and Multiagent SystemsIn this paper, we present the first sublinear α-regret bounds for online k-submodular optimization problems with full-bandit feedback, where α is a corresponding offline approximation ratio. Specifically, we propose online algorithms for multiple k-submodular stochastic combinatorial multi-armed bandit problems, including (i) monotone functions and ...
Guanyu Nie +2 more
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HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems
IEEE Open Journal of the Computer SocietyHyperdimensional Computing (HDC), or Vector Symbolic Architectures, is a computing paradigm that combines symbolic reasoning with the efficiency of distributed representations.
M. Angioli +5 more
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, 2019 « La défaite et la mort du bandit, c'est la défaite de son peuple, et qui pis est, la mort de l'espoir » écrit Eric John Hobsbawm au huitième chapitre des Bandits, dont la quatrième édition est parue en 2008. Champion des pauvres, réputé invulnérable, le
Célérier, Laure
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