Results 11 to 20 of about 497,803 (209)
Post-Contextual-Bandit Inference. [PDF]
Adv Neural Inf Process Syst, 2021 Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because they can both improve outcomes for study participants and increase the chance of identifying good or even best policies.
Bibaut A +4 more
europepmc +9 more sources
A Contextual Bandit Bake-off [PDF]
J. Mach. Learn. Res., 2018 JMLR
Bietti, Alberto +2 more
openaire +7 more sources
Expected Improvement for Contextual Bandits
Advances in Neural Information Processing Systems 35, 2022 Expected Improvement for Contextual ...
Hung Tran-The +5 more
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Encrypted Linear Contextual Bandit
, 2022 International audienceContextual bandit is a general framework for online learning in sequential decision-making problems that has found application in a wide range of domains, including recommendation systems, online advertising, and clinical trials.
Garcelon, Evrard +2 more
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On Submodular Contextual Bandits
CoRR, 2021 We consider the problem of contextual bandits where actions are subsets of a ground set and mean rewards are modeled by an unknown monotone submodular function that belongs to a class $\mathcal{F}$. We allow time-varying matroid constraints to be placed on the feasible sets.
Dean P. Foster, Alexander Rakhlin
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Smoothness-Adaptive Contextual Bandits [PDF]
Operations Research, 2021 We study a non-parametric multi-armed bandit problem with stochastic covariates, where a key complexity driver is the smoothness of payoff functions with respect to covariates. Previous studies have focused on deriving minimax-optimal algorithms in cases where it is a priori known how smooth the payoff functions are.
Yonatan Gur +2 more
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2022 IEEE International Symposium on Information Theory (ISIT), 2022 We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel. This can model, for example, a personalized ad placement application, where the content owner observes the individual ...
Francesco Pase +2 more
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Selectively Contextual Bandits
CoRR, 2022 Contextual bandits are widely used in industrial personalization systems. These online learning frameworks learn a treatment assignment policy in the presence of treatment effects that vary with the observed contextual features of the users. While personalization creates a rich user experience that reflect individual interests, there are benefits of a ...
Claudia V. Roberts +4 more
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Contextual combinatorial conservative bandits
Theoretical Computer Science, 2023 The problem of multi-armed bandits (MAB) asks to make sequential decisions while balancing between exploitation and exploration, and have been successfully applied to a wide range of practical scenarios. Various algorithms have been designed to achieve a high reward in a long term.
Xiaojin Zhang +3 more
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CoRR, 2020 We study a novel variant of the multi-armed bandit problem, where at each time step, the player observes an independently sampled context that determines the arms' mean rewards. However, playing an arm blocks it (across all contexts) for a fixed and known number of future time steps. The above contextual setting, which captures important scenarios such
Soumya Basu 0001 +3 more
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