This is a joint Call for Papers for the Research and the Applied Data Science Tracks at ECMLPKDD 2023.
In the Research Track, we invite submissions of research papers from all areas of knowledge discovery, data mining, and machine learning. We are looking for high-quality papers in terms of novelty, technical quality, potential impact, and clarity of presentation. Papers should demonstrate that they make a significant contribution to the field (e.g., improve the state-of-the-art or provide a new theoretical insight).
In the Applied Data Science Track, we invite submissions that present compelling applications of knowledge discovery, data mining, and machine learning to solve challenging and important real-world problems, thereby bridging the gap between practice and theory. Papers should clearly explain the real-world challenge addressed (including any peculiarities of the data, such as the size of the dataset or noise levels), the methodology used, and the conclusions and implications that are drawn for the use case. If the paper presents a deployed solution, explicitly mention it in the paper and provide any relevant details.
Key Dates and Deadlines
- Abstract submission deadline: March 26, 2023
- Paper submission deadline: April 2, 2023
- Author notification: June 5, 2023
- Camera-ready submission: June 23, 2023
- All deadlines expire at 23:59 AoE (UTC – 12)
- Conference: 18-22 September, 2023
Papers must be written in English and formatted according to the Springer LNCS guidelines. Author instructions, style files, and the copyright form can be downloaded here.
The maximum length of papers is 14 pages for the technical content excluding references in this format. The space for references is not limited. In addition, there is one page for the discussion of ethical issues (ethical statement, see below). Papers that exceed this limit will be desk rejected. Egregious changes to the format to ‘cheat’ the page limit may also lead to desk-rejection.
Up to 10 MB of additional materials (e.g. proofs, audio, images, video, data, or source code) can be uploaded with your submission. The reviewers and the program committee reserve the right to judge the paper solely on the basis of the main paper; looking at any additional material is at the discretion of the reviewers and is not required.
All papers need to be ‘best-effort’ anonymized. We strongly encourage making code and data available anonymously (e.g., in an anonymous GitHub repository via Anonymous GitHub or in a Dropbox folder). The authors may have a (non-anonymous) pre-print published online, but it should not be cited in the submitted paper to preserve anonymity. Reviewers will be asked not to search for them.
Electronic submissions will be handled via CMT available at the following address https://cmt3.research.microsoft.com/ECMLPKDD2023. Before submitting, please consider carefully what the appropriate track is (see the top of the page). The track of choice can be indicated in the submission form. Submissions will be assessed in the track where they were submitted and cannot be transferred across tracks.
Double-blind Reviewing Process
Submissions will be evaluated by at least three reviewers on the basis of relevance, technical quality, potential impact, and clarity. ECMLPKDD has a long-standing reputation for being a truly diverse conference where many topics in Machine Learning and Data Mining are represented. Thus, the selection process will also take this factor into consideration.
The reviewing process is double-blind (reviewers and area chairs are not aware of the identities of the authors; reviewers can see each other’s names). Papers must not include identifying information of the authors (names, affiliations, etc.), self-references, or links (e.g., GitHub, YouTube) that reveal the authors’ identities (e.g., references to own work should be given neutrally like other references, not mentioning ‘our previous work’ or similar). However, we recognize there are limits to what is feasible with respect to anonymization. For example, if you use data from your own organization and it is relevant to the paper to name this organization, you may do so.
The conference proceedings will be published by Springer in the Lecture Notes in Computer Science Series (LNCS). For each accepted paper, at least one author must register for the conference and present the paper in presence at the conference venue.
Reproducible Research Papers
Authors are strongly encouraged to adhere to the best practices of Reproducible Research (RR), by making available data and software tools that enable others to reproduce the results reported in their papers. Authors may flag their submissions as RR and make software and data accessible to reviewers who will verify their accessibility. Links to data and code must be inserted in the final version of RR papers. For accepted papers, we require the use of standard repository hosting services such as Dataverse, mldata.org, OpenML, figshare, or Zenodo for datasets, 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 repositories and research data policy.
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, e.g., related to the collection and processing of personal data, the inference of personal information, or the potential use of your work for policing or the military. which will be taken into consideration by the reviewers.As part of your submission, you are asked to include an ethical statement up to one page in length that discusses any ethical implications of your work.
Best Paper Awards
Among the best paper awards, there will be two student best paper prizes awarded at the conference and sponsored by Springer’s Data Mining and Machine Learning journals. In order to be eligible for these awards, the first author of the paper needs to have been a (PhD) student on the day of the submission deadline: April 2, 2023.
Best Reviewer Awards
We would like to recognize the reviewing efforts of our Program Committee. Thus, we will ask Associate Chairs to flag high-quality reviews that provide valuable feedback to authors in a constructive way. The Chairs will publish a list of the best reviewers for the conference.
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 ECMLPKDD, and the organizers will reject such papers without review. Authors are also not allowed to submit their papers elsewhere during the review period. The dual submission policy applies during the period April 2 – June 5, 2023.
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.
The author list as submitted with the paper is considered final. No changes to this list may be made after paper submission, either during the reviewing period or, in case of acceptance, at the final camera-ready stage.
Conflicts 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 if 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, being colleagues in the same institution within the last three years, and advisor/student relations irrespective of how much time has passed.