Nearest Neighbour with Bandit Feedback

• URL: http://arxiv.org/abs/2306.13773v3
• Date: Thu, 7 Mar 2024 21:07:35 GMT
• Title: Nearest Neighbour with Bandit Feedback
• Authors: Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
• Abstract summary: Our algorithm handles the fully adversarial setting in which no assumptions at all are made about the data-generation process. We give generic regret for our algorithm and further analyse them when applied to the bandit problem in euclidean space.
• Score: 4.9094025705644695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we adapt the nearest neighbour rule to the contextual bandit problem. Our algorithm handles the fully adversarial setting in which no assumptions at all are made about the data-generation process. When combined with a sufficiently fast data-structure for (perhaps approximate) adaptive nearest neighbour search, such as a navigating net, our algorithm is extremely efficient - having a per trial running time polylogarithmic in both the number of trials and actions, and taking only quasi-linear space. We give generic regret bounds for our algorithm and further analyse them when applied to the stochastic bandit problem in euclidean space. We note that our algorithm can also be applied to the online classification problem.

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