[2302.03784] Leveraging User-Triggered Supervision in Contextual Bandits
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Computer Science > Machine Learning

arXiv:2302.03784 (cs)
[Submitted on 7 Feb 2023]

Title:Leveraging User-Triggered Supervision in Contextual Bandits

Authors:Alekh Agarwal, Claudio Gentile, Teodor V. Marinov
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Abstract:We study contextual bandit (CB) problems, where the user can sometimes respond with the best action in a given context. Such an interaction arises, for example, in text prediction or autocompletion settings, where a poor suggestion is simply ignored and the user enters the desired text instead. Crucially, this extra feedback is user-triggered on only a subset of the contexts. We develop a new framework to leverage such signals, while being robust to their biased nature. We also augment standard CB algorithms to leverage the signal, and show improved regret guarantees for the resulting algorithms under a variety of conditions on the helpfulness of and bias inherent in this feedback.
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2302.03784 [cs.LG]
  (or arXiv:2302.03784v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2302.03784

Submission history

From: Teodor Vanislavov Marinov [view email]
[v1] Tue, 7 Feb 2023 22:42:27 UTC (65 KB)
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