We are excited to announce a call for the Hot-off-the-Press Track for the 2026 AutoML Conference in Ljubljana. This track provides an opportunity to present recently published papers from top-tier journals and conferences relevant to the AutoML community.
Submission Guidelines
In contrast to the late-breaking abstract, methods and ABCD track, the reviewing process for this track will be lightweight, with the primary criterion for acceptance being relevance and interest to the AutoML community. All accepted submissions will be presented as posters at the conference and will be announced and advertised on the conference website, however, they will not be archived in the proceedings of the conference.
To be admissible to this track, the following conditions have to be met:
1) The paper must be within the scope of AutoML (see the main CfP for a list of possible topics).
2) The paper was accepted at a peer-reviewed top-tier journal or conference in machine learning in 2025 and 2026.
3) The paper must be published under an open-access license (i.e., readers do not have to pay or register in any form to read the paper).
In view of Conditions 2 and 3, we consider journals such as JMLR, DMLR, TMLR, and JAIR to be top-tier open-access journals, and conferences such as ICML, NeurIPS, and ICLR to be top-tier conferences. Papers recently published in these journals or conferences directly qualify for this track. For other top-tier journals, such as, e.g., IEEE TPAMI, AIJ or MLJ, papers only qualify if the open-access option was purchased. We note that the paper must be available as open access at the time of submission.
Authors of accepted papers must present their work during the poster session, just like authors of papers in the Methods and ABCD tracks.
Submission Instructions and Time Line
* Submission Deadline: August 3rd, 2026 (AoE)
* Author notification: August 14th, 2026
* Submission link: https://forms.gle/shpL44coSwiWLJkC6
The submission process is fairly lightweight: Authors should provide a link to a publicly accessible version of the accepted paper, meta-data and a short statement (<250 words; submitted) explaining why the AutoML community should be excited about it.