Efficient pure exploration in adaptive round model

Tianyuan Jin, Jieming Shi, Xiaokui Xiao, Enhong Chen

Research output: Journal article publicationConference articleAcademic researchpeer-review

3 Citations (Scopus)


In the adaptive setting, many multi-armed bandit applications allow the learner to adaptively draw samples and adjust sampling strategy in rounds. In many real applications, not only the query complexity but also the round complexity need to be optimized. In this paper, we study both PAC and exact top-k arm identification problems and design efficient algorithms considering both round complexity and query complexity. For PAC problem, we achieve optimal query complexity and use only O(log*k d (n)) rounds, which matches the lower bound of round complexity, while most of existing works need T(log nk ) rounds. For exact top-k arm identification, we improve the round complexity factor from log n to log*d 1 (n), and achieve near optimal query complexity. In experiments, our algorithms conduct far fewer rounds, and outperform state of the art by orders of magnitude with respect to query cost.

Original languageEnglish
JournalAdvances in Neural Information Processing Systems
Publication statusPublished - 2019
Externally publishedYes
Event33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver, Canada
Duration: 8 Dec 201914 Dec 2019

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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