IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v50y2023i3p1298-1324.html
   My bibliography  Save this article

Daisee: Adaptive importance sampling by balancing exploration and exploitation

Author

Listed:
  • Xiaoyu Lu
  • Tom Rainforth
  • Yee Whye Teh

Abstract

We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade‐off between exploration and exploitation in this adaptation. Borrowing ideas from the online learning literature, we propose Daisee, a partition‐based AIS algorithm. We further introduce a notion of regret for AIS and show that Daisee has 𝒪(T(logT)34) cumulative pseudo‐regret, where T$$ T $$ is the number of iterations. We then extend Daisee to adaptively learn a hierarchical partitioning of the sample space for more efficient sampling and confirm the performance of both algorithms empirically.

Suggested Citation

  • Xiaoyu Lu & Tom Rainforth & Yee Whye Teh, 2023. "Daisee: Adaptive importance sampling by balancing exploration and exploitation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 1298-1324, September.
    • Handle: RePEc:bla:scjsta:v:50:y:2023:i:3:p:1298-1324
    DOI: 10.1111/sjos.12637
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/sjos.12637
    Download Restriction: no
    File URL: https://libkey.io/10.1111/sjos.12637?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:scjsta:v:50:y:2023:i:3:p:1298-1324. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.