Computer Science > Machine Learning
[Submitted on 13 Nov 2018 (v1), last revised 18 Nov 2018 (this version, v2)]
Title:Community Exploration: From Offline Optimization to Online Learning
View PDFAbstract:We introduce the community exploration problem that has many real-world applications such as online advertising. In the problem, an explorer allocates limited budget to explore communities so as to maximize the number of members he could meet. We provide a systematic study of the community exploration problem, from offline optimization to online learning. For the offline setting where the sizes of communities are known, we prove that the greedy methods for both of non-adaptive exploration and adaptive exploration are optimal. For the online setting where the sizes of communities are not known and need to be learned from the multi-round explorations, we propose an `upper confidence' like algorithm that achieves the logarithmic regret bounds. By combining the feedback from different rounds, we can achieve a constant regret bound.
Submission history
From: Xiaowei Chen [view email][v1] Tue, 13 Nov 2018 07:18:38 UTC (1,198 KB)
[v2] Sun, 18 Nov 2018 07:01:47 UTC (1,198 KB)
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