Key Dates and Deadlines

Given the special nature of the journal track, submitted papers must adhere to the following eligibility criteria:

• Papers must not be under review elsewhere at any time between submission and notification.
• Papers must satisfy the high-quality criteria of the journals involved and, at the same time, lend themselves to conference talks.
• Journal versions of previously published conference papers and survey papers will not be considered for the special issue.
• Papers rejected by a previous submission deadline may not be resubmitted to any subsequent deadline regardless of modifications. Additionally, papers rejected by the Machine Learning Journal may not be resubmitted to the Data Mining and Knowledge Discovery journal, and vice versa.
• Papers that do not fall into the eligible category may be rejected without formal reviews but can be resubmitted as regular papers to the Springer journals.
• Authors are encouraged to adhere to the best practices of Reproducible Research (RR), by making available data and software tools for reproducing the results reported in their papers. For the sake of persistence and proper authorship attribution, we require the use of standard repository hosting services, e.g. dataverse, mldata, openml, mloss, bitbucket, github for source code.

Authors who submit their work to the ECML PKDD 2026 journal track with these journals commit themselves to presenting their paper at the ECML PKDD conference if it is accepted.

The journal track allows continuous submissions from September 2025 to15th of January 2026. Papers will be processed and sent out for review after each of the following two cutoff dates:

• October 30, 2025

• January 15, 2026

   

The deadline on each of these dates is 23:59, Anywhere on Earth (AoE). We strive for a high-quality and efficient review process. For each submission, we aim to obtain three reviews from experienced reviewers, including members of the Guest Editorial Board. Our goal is to arrive at an initial decision about 10 weeks after each cutoff date, though meeting this target may not always be possible. Papers not rejected in the initial review phase normally require (sometimes substantial) revisions, and the revised paper will be re-reviewed, which extends the review process. Consequently, a paper’s chance of finishing the full review cycle and being included in the ECML PKDD 2026 special issue decreases with a subsequent cutoff date. This means that accepted papers, especially those that were submitted to the later deadline, may be included in the ECML PKDD 2027 (or even later) special issue (subject to the approval of the ECML PKDD steering committee and future organizers). The reviewing process is single-blind (so the authors’ identities are disclosed to the anonymous reviewers).

To facilitate both the bidding process for reviewers and journal-style reviewing, authors submitting to the ECML PKDD 2026 Journal Track must complete two submission processes. Each manuscript must be submitted both to the CMT system used by the ECML PKDD conference and to the relevant journal's dedicated SNAPP submission platform. The CMT system will be used solely for reviewer bidding and conflict-of-interest management, while the full review and editorial process will take place entirely within the journal’s system. Specifically,

• Step 1: Submit Title and Abstract to the CMT System [HERE] by adding the author names and relevant keywords and indicating which of the two journals the paper is intended to.
• Submit the Full Paper to SNAPP (Selected Journal Only) choosing the Collection ECMLPKDD 2026 during the submission:
  1. Submit to the Springer Data Mining and Knowledge Discovery journal [HERE] or
  2. Submit to the Springer Machine Learning journal [HERE]

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.

Both submissions must be completed by the journal track deadline. No paper can be submitted to both journals. All communications to authors will be sent by SNAPP and exclusively to the corresponding author. Each paper has exactly one corresponding author, who is determined upon submission and cannot be changed later. Check carefully all responsibilities of corresponding author at https://support.springernature.com/en/support/solutions/articles/6000214118-first-author-and-corresponding-author-defined

Incomplete submissions (missing either the submission in the SNAPP system of the selected journal or the submission in the CMT submission system), as well as double submissions to both journals will be desk rejected. Papers MUST be prepared in English and formatted following the submission guidelines on the respective journals’ home pages (Submission guidelines for Data Mining and Knowledge Discovery, Submission guidelines for Machine Learning). Templates can be found at https://www.springernature.com/gp/authors/campaigns/latex-author-support.

It is highly recommended that submitted papers do not exceed 20 pages, including references. Papers exceeding 23 pages will be desk rejected. Papers with 21 and 22 pages will be scrutinized and may be desk rejected whenever extra pages are not used for references, proofs, declarations. Every paper may be accompanied by unlimited appendices (as a part of the same file).

Both journals require authors to include an information sheet (for Machine Learning submissions) or a cover letter (up to 2 pages) as supplementary material (for Data Mining and Knowledge Discovery submissions) 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/data mining research area?
• What is the evidence provided to support claims? Be precise.
• Report 3-5 most closely related contributions in the past 7 years (authored by researchers outside the authors’ research group) and briefly state the relation of the submission to them.
• Specify 5 general keywords and 5 specific keywords describing the main research activity presented in the manuscript.
• Declare any conflict of interest by reporting the email domains of all institutions with which the authors have an institutional conflict of interest. Authors have an institutional conflict of interest if they are currently employed or have been employed at this institution in the past three years, or if the authors have extensively collaborated with this institution within the past three years. Authors are also required to identify all Guest Editorial Board Members with whom the authors have a conflict of interest. Examples of conflicts of interest include co-authorship in the last five years, a colleague in the same institution within the last five years, advisor/student relationships, parentage relationships.
• Who are the most appropriate reviewers for the paper? Authors are required to suggest up to four candidate reviewers (especially if external to the Guest Editorial Board), including a brief motivation for each suggestion. Optionally, list up to four researchers/potential reviewers with competing interests that should not be considered for reviewers. PhD students cannot be suggested as potential reviewers. For each cut-off date, there is paper bidding.

Research reported at ECML-PKDD should avoid harm, be honest and trustworthy, fair and non-discriminatory, and respect privacy and intellectual property. Where relevant, authors can include in the main body of their paper, or on the reference page, a short ethics statement that addresses ethical issues regarding the research being reported and the broader ethical impact of the work. Reviewers will be asked to flag possible violations of relevant ethical principles. Such flagged submissions will be reviewed by a senior member of the programme committee. Authors may be required to revise their paper to include a discussion of possible ethical concerns and their mitigation.

In particular, ECML-PKDD subscribes to the European Code of Conduct for Research Integrity. By submitting to ECML-PKDD you confirm that you are aware of and accept the following specific policies:

• Originality: Submissions must not have substantial overlap in either contribution or text with work previously accepted for publication as a full paper in another archival forum. Papers at workshops without archival proceedings and preprints are fine.
• Concurrent submission: We recognise the significant strain on the scientific community caused by the needs of peer review. Therefore, the work you submit must not be under review elsewhere at any time between submission and notification.
• Single-Blind Review: Reviewing for ECML-PKDD is single-blind, meaning that the identities of the reviewers are not disclosed to the authors.
• Links: Papers must not include pointers to supplementary material on the web, not even when that supplementary material has been fully anonymised, as we would be unable to ensure that it remains unaltered throughout the reviewing period.
• Reproducibility: Reviewers will be instructed to pay close attention to reproducibility of results where appropriate and you should submit relevant code and data as supplementary material whenever feasible.
• Information sharing: Submissions will be treated confidentially. However, papers, author information, and reviews may be shared with the organisers of other AI conferences to identify duplicate submissions and to limit duplicate reviewing efforts.
• Abstracts: Abstracts are central for the assignment of reviewers. Therefore, they must not be altered in any significant way after the abstract submission deadline. In particular, submitting “placeholder abstracts” is not admissible.
• Authorship: All individuals, and only those, who have made significant contributions to a paper should be listed as authors in the submission system. We will not permit adding or removing authors to a paper after the submission deadline. Please also ensure that the order of authors is correct at submission time.
• Generative AI: The use of AI systems to generate text for inclusion in an ECML-PKDD submission is only allowed if its role is properly documented in the manuscript (e.g., when reporting on experiments on such systems). However, the use of AI-powered systems to assist with the polishing of human-authored text is permissible. In any case, the use of Generative AI must be documented by the authors in the cover letter.
• Attendance: At least one author of each accepted paper must register for the conference by the early registration deadline with the intention of presenting the paper at the conference. This is a prerequisite for inclusion in the proceedings.
• Reviewing: To ensure that all papers receive a sufficient number of high quality reviewers, there is a requirement for authors to contribute to reviewing. Every submission must nominate at least one author who is a qualified reviewer (i.e., authors with at least two papers in ECML-PKDD or other top-ranked related conferences or journals). Only if no qualified reviewer exists in the author list, the authors should nominate the best-qualified author for consideration by the PC chairs. In addition, any author listed on 3 or more papers will be automatically signed up as a reviewer unless they are already serving as a reviewer, AC, or SAC or they are not sufficiently qualified to serve on the PC (such as students). Either case above constitutes an acceptance and a commitment to carry out the regular reviewing load responsibly. Failure to provide a qualified reviewer when one exists in the author list, or failure to carry out the assigned reviewing duty properly, is grounds for desk rejection.