Call for Special Issue

As a follow-up to our announcement during the DS23 conference, we can officially announce that the Special Issue on Machine Learning journal for the 26th edition of the Discovery Science conference is now open. You can find the call for papers at this link:

We invite authors of the papers accepted in Discovery Science 2023 to submit to this Special Issue. The submission should have a significant contribution beyond the conference paper, containing at least 30% of new material (e.g., extensions of the method, additional technical results, etc.) compared to the paper’s conference version.

The guest editors (accounting for reviewers’ comments) will decide whether the difference is significant enough to warrant publication. The journal version should include a short paragraph explaining how it extends the previously published conference paper.


  • Paper submission deadline: March 4, 2024
  • First notification of acceptance: May 27, 2024
  • Deadline for revised submissions: July 15, 2024
  • Final notification of acceptance: August 30, 2024
  • Expected publication date (online): October/November 2024

Submission procedure

To submit to this issue, authors have to make a journal submission to the Springer Machine Learning journal ( and select the type of submission to be for the “S.I.: Discovery Science 2023” special issue. It is highly recommended that submitted papers do not exceed 20 pages, including references. Every paper may be accompanied by unlimited appendices.

The papers should be formatted in the Springer journal style (svjour3, small condensed). The journal requires authors to include an information sheet as supplementary material that contains a short summary of their contribution and specifically addresses the following questions:

  • What is the main claim of the paper?
  • Why is this an important contribution to the machine learning literature? [“We are the first to have done X” is not an acceptable answer without stating the importance of X.]
  • What is the evidence you provide to support your claim? Be precise. [“The evidence is provided by experiments and/or theoretical analysis” is not an acceptable answer without a summary of the main results and their implications.]
  • What papers by other authors make the most closely related contributions, and how is your paper related to them?
  • Have you published parts of your paper before, for instance, in a conference? If so, give details of your previous paper(s) and a precise statement detailing how your paper provides a significant contribution beyond the previous paper(s).

Guest editors

  • Rita P. Ribeiro, University of Porto & INESC TEC, Portugal
  • Albert Bifet, University of Waikato, New Zealand
  • Ana Carolina Lorena, Aeronautics Institute of Technology, Brazil

For Queries relating to Journal Track Submissions, email the journal track chairs at,,