Results 181 to 190 of about 492,986 (210)
A habit and working memory model as an alternative account of human reward-based learning. [PDF]
Nat Hum BehavCollins AGE.
europepmc +1 more source
Contextual Bandits With Cross-Learning
Mathematics of Operations Research, 2023 In the classic contextual bandits problem, in each round t, a learner observes some context c, chooses some action i to perform, and receives some reward [Formula: see text]. We consider the variant of this problem in which in addition to receiving the reward [Formula: see text], the learner also learns the values of [Formula: see text] for some other
Mohammad Mahdian +2 more
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Adaptive Metamorphic Testing With Contextual Bandits [PDF]
Journal of Systems and Software, 2020 Metamorphic Testing is a software testing paradigm which aims at using necessary properties of a system-under-test, called metamorphic relations, to either check its expected outputs, or to generate new test cases. Metamorphic Testing has been successful to test programs for which a full oracle is not available or to test programs for which there are ...
Arnaud Gotlieb, Helge Spieker
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Linear Bayes Policy for Learning in Contextual-Bandits [PDF]
Expert Systems With Applications, 2013 Machine and Statistical Learning techniques are used in almost all online advertisement systems. The problem of discovering which content is more demanded (e.g. receive more clicks) can be modeled as a multi-armed bandit problem. Contextual bandits (i.e.,
José Antonio Martín H
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Contextual Bandits in a Collaborative Environment
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016Contextual bandit algorithms provide principled online learning solutions to find optimal trade-offs between exploration and exploitation with companion side-information. They have been extensively used in many important practical scenarios, such as display advertising and content recommendation.
Qingyun Wu +3 more
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Mixed-Effects Contextual Bandits
Proceedings of the AAAI Conference on Artificial IntelligenceWe study a novel variant of a contextual bandit problem with multi-dimensional reward feedback formulated as a mixed-effects model, where the correlations between multiple feedback are induced by sharing stochastic coefficients called random effects.
Kyungbok Lee +3 more
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Mostly Exploration-Free Algorithms for Contextual Bandits
Management Science, 2021Mohsen Bayati +2 more
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