The Research track solicits high-quality research papers in all fields of Machine Learning, Knowledge Discovery, and Data Mining. Papers should demonstrate that they make a substantial contribution to the field (e.g., improve the state-of-the-art or provide new theoretical insights) and will be evaluated based on their contribution to the state of the art, technical excellence, potential impact, and clarity.

Reproducible Research Papers

Authors are strongly encouraged to adhere to the best practices of Reproducible Research, by making available data and software tools that would enable others to reproduce the results reported in their papers. We advise the use of standard repository hosting services such as Dataverse, mldata.org, OpenML, figshare, or Zenodo for data sets, and mloss.org, Bitbucket, GitHub, or figshare (where it is possible to assign a DOI) for source code. If data or code gets updated after the paper is published, it is important to enable researchers to access the versions that were used to produce the results reported in the paper. Authors who do not have a preferred repository are advised to consult Springer Nature’s list of recommended repositories and research data policy.

Ethics considerations

Ethics is one of the most important topics to emerge in Machine Learning and Data Mining. We ask you to think about the ethical implications of your submission – such as those related to the collection and processing of personal data or the inference of personal information, the potential use of your work for policing or the military. You will be asked in the submission form about the ethical implications of your work which will be taken into consideration by the reviewers.

Authors commit to reviewing

Authors of submitted papers agree to be potential PC members/reviewers for ECML PKDD 2024 and may be asked to review papers for the conference. This does not apply to authors who are (a) already contributing to ECML PKDD (e.g., accepted a PC/AC invite, are part of the organizing committee) or (b) not qualified to be ECML PKDD PC members (e.g., limited background in ML or DM). This requirement can be waived in a limited range of exceptional cases (e.g., parental leave, long-term illness).

Dual Submission Policy

Papers submitted should report original work. Papers that are identical or substantially similar to papers that have been published or submitted elsewhere may not be submitted to ECML PKDD, and the organizers will reject such papers without review. Authors are also NOT allowed to submit or have submitted their papers elsewhere during the review period. Submitting unpublished technical reports available online (such as on arXiv), or papers presented in workshops without formal proceedings, is allowed, but such reports or presentations should not be cited to preserve anonymity.

Conflict of Interest

During the submission process, you must enter the email domains of all institutions with which you have an institutional conflict of interest. You have an institutional conflict of interest if you are currently employed or have been employed by that institution in the past three years, or you have extensively collaborated with the institution within the past three years. Authors are also required to identify all Program Committee Members and Area Chairs with whom they have a conflict of interest. Examples of conflicts of interest include: co-authorship in the last five years, colleague in the same institution within the last three years, and advisor/student relations (anytime in the past).