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arxiv: 1508.03326 · v2 · pith:5ET44QHFnew · submitted 2015-08-13 · 💻 cs.LG

A Survey on Contextual Multi-armed Bandits

Li Zhou This is my paper
classification 💻 cs.LG
keywords contextualsurveyadversarialalgorithmalgorithmsanalyzeassumptionbandit
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In this survey we cover a few stochastic and adversarial contextual bandit algorithms. We analyze each algorithm's assumption and regret bound.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    Latent order bandits require only a known partial order on actions within each latent state rather than full reward distributions, enabling UCB and posterior-sampling algorithms with regret bounds that match or exceed...

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    Identifiable latent bandits apply nonlinear ICA to observational data to recover representations sufficient for inferring optimal actions in new instances, shortening exploration time.