IDEAS home Printed from https://ideas.repec.org/p/chf/rpseri/rp2220.html
   My bibliography  Save this paper

Deep Regression Ensembles

Author

Listed:
  • Antoine Didisheim

    (Swiss Finance Institute, UNIL)

  • Bryan T. Kelly

    (Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER))

  • Semyon Malamud

    (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute)

Abstract

We introduce a methodology for designing and training deep neural networks (DNN) that we call “Deep Regression Ensembles" (DRE). It bridges the gap between DNN and two-layer neural networks trained with random feature regression. Each layer of DRE has two components, randomly drawn input weights and output weights trained myopically (as if the final output layer) using linear ridge regression. Within a layer, each neuron uses a different subset of inputs and a different ridge penalty, constituting an ensemble of random feature ridge regressions. Our experiments show that a single DRE architecture is at par with or exceeds state-of-the-art DNN in many data sets. Yet, because DRE neural weights are either known in closed-form or randomly drawn, its computational cost is orders of magnitude smaller than DNN.

Suggested Citation

  • Antoine Didisheim & Bryan T. Kelly & Semyon Malamud, 2022. "Deep Regression Ensembles," Swiss Finance Institute Research Paper Series 22-20, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2220
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4049493
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Thomas Wong & Mauricio Barahona, 2023. "Deep incremental learning models for financial temporal tabular datasets with distribution shifts," Papers 2303.07925, arXiv.org, revised Oct 2023.

    More about this item

    Keywords

    Deep learning; Neural network; Random features; Ensembles;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:chf:rpseri:rp2220. 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: Ridima Mittal (email available below). General contact details of provider: https://edirc.repec.org/data/fameech.html .

    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.