Results 101 to 110 of about 3,478,919 (201)
Active Learning in Multi-armed Bandits [PDF]
, 2008 In this paper we consider the problem of actively learning the mean values of distributions associated with a finite number of options (arms). The algorithms can select which option to generate the next sample from in order to produce estimates with equally good precision for all the distributions.
András Antos +2 more
openaire +1 more source
, 2012 This paper reports on the development of a multi–agent approach to long-term information collection in networks of energy harvesting wireless sensors.
Jennings, Nick +2 more
core +1 more source
NeIL: Intelligent Replica Selection for Distributed Applications
IEEE Transactions on Machine Learning in Communications and NetworkingDistributed applications such as cloud gaming, streaming, etc., are increasingly using edge-to-cloud infrastructure for high availability and performance.
Faraz Ahmed +3 more
doaj +1 more source
Multiple Identifications in Multi-Armed Bandits
CoRR, 2012 We study the problem of identifying the top $m$ arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that were previously out of reach.
Sébastien Bubeck +2 more
openaire +3 more sources
Adversarially robust stochastic multi-armed bandits
, 2020 Multi-armed bandits are a fundamental problem in sequential decision making with applications in optimal experimental design, online advertisement, recommender systems and much more.
Zimmert, Julian Ulf
core +1 more source
Two-stage index computation for bandits with switching penalties II : switching delays [PDF]
This paper addresses the multi-armed bandit problem with switching penalties including both costs and delays, extending results of the companion paper [J. Niño-Mora.
Jose Nino-Mora
core
Negatively Correlated Bandits [PDF]
We analyze a two-player game of strategic experimentation with two-armed bandits. Each player has to decide in continuous time whether to use a safe arm with a known payoff or a risky arm whose likelihood of delivering payoffs is initially unknown.
Sven Rady, Nicolas Klein
core
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
doaj +1 more source
Precision Agriculture Optimization based on Multi-Armed Bandits Algorithm: Wheat Yield Optimization under Different Temperature and Precipitation Conditions [PDF]
ITM Web of ConferencesClimate change and the growing unpredictability of environmental elements such as temperature and precipitation present considerable challenges to contemporary agriculture. Data-driven algorithms present promising solutions by offering more precise tools
Huang Qikang
doaj +1 more source
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits [PDF]
The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $N$ agents such that each agent is learning one of $M$ stochastic multi ...
Srikant, R. +3 more
core +1 more source