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The Perils of Misspecified Priors and Optional Stopping in Multi-Armed Bandits [PDF]
Frontiers in Artificial Intelligence, 2021 The connection between optimal stopping times of American Options and multi-armed bandits is the subject of active research. This article investigates the effects of optional stopping in a particular class of multi-armed bandit experiments, which ...
Markus Loecher
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An Analysis of the Value of Information When Exploring Stochastic, Discrete Multi-Armed Bandits [PDF]
Entropy, 2018 In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion.
Isaac J Sledge, JOSÉ C Principe
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Multi-Armed Bandits in Brain-Computer Interfaces. [PDF]
Front Hum Neurosci, 2022 The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward.
Heskebeck F +2 more
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Online Multi-Armed Bandit [PDF]
CoRR, 2017 We introduce a novel variant of the multi-armed bandit problem, in which bandits are streamed one at a time to the player, and at each point, the player can either choose to pull the current bandit or move on to the next bandit. Once a player has moved on from a bandit, they may never visit it again, which is a crucial difference between our problem ...
Uma Roy, Ashwath Thirmulai, Joe Zurier
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Be Greedy in Multi-Armed Bandits [PDF]
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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Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits. [PDF]
Entropy (Basel)We consider the distributed stochastic gradient descent problem, where a main node distributes gradient calculations among n workers. By assigning tasks to all workers and waiting only for the k fastest ones, the main node can trade off the algorithm’s ...
Egger M +3 more
europepmc +2 more sources
Introduction to Multi-Armed Bandits [PDF]
Foundations and Trends in Machine Learning, 2019 Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject.
Aleksandrs Slivkins
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Study of multi-armed bandits for energy conservation in cognitive radio sensor networks. [PDF]
Sensors (Basel), 2015 Technological advances have led to the emergence of wireless sensor nodes in wireless networks. Sensor nodes are usually battery powered and hence have strict energy constraints.
Zhang J +4 more
europepmc +2 more sources
Designing digital health interventions with causal inference and multi-armed bandits: a review. [PDF]
Front Digit HealthRecent statistics from the World Health Organization show that non-communicable diseases account for 74% of global fatalities, with lifestyle playing a pivotal role in their development. Promoting healthier behaviors and targeting modifiable risk factors
Švihrová R +4 more
europepmc +2 more sources
Optimizing the ε Parameter in ε-Greedy Strategy for Multi-Armed Bandits [PDF]
ITM Web of ConferencesWith the widespread adoption of the internet, online advertising has grown exponentially. To enhance ad recommendation efficiency, various Multi-Armed Bandit (MAB) algorithms have been deployed. Among these, the Thompson ε-Greedy algorithm integrates the
Haotong Jiang
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