Call for Papers

We invite papers that describe innovative combinations of symbolic knowledge representation/reasoning approaches and learning: including new methodology, datasets, evaluations, surveys, reproduced results, and negative results.

The KR2ML workshop is intended to provide a forum for discussing new approaches and challenges in integrating KRR and ML approaches, and for exchanging ideas about how to move the area forward.

Topics of interest for all kinds of submissions include, but are not limited to:

Important DatesĀ 

Submission: October 09 (11:59pm Pacific Time)
Updates to existing papers possible until October 12 (11:59pm Pacific Time)
Notification: October 30
Workshop: December 11

Submission Requirements

Submissions to KR2ML 2020 are limited to 4 pages of content, but may contain an unlimited number of pages for references and appendices. The latter may not necessarily be read by the reviewers. We request and recommend that authors rely on the supplementary material only to include minor details (e.g., hyperparameter settings, reproducibility information, etc.) that do not fit in the 4 pages. The submission process is double-blind.

All submissions must be formatted with LaTeX using the NeurIPS paper format (adapted).

All accepted papers will be presented in a virtual poster session, and some will be selected for oral presentation. We welcome articles currently under review or papers planned for publication elsewhere. However, papers that have been published at an ML conference should not be submitted. Accepted papers will be published on the KR2ML homepage, but are to be considered non-archival.

Submission Link: https://cmt3.research.microsoft.com/KR2ML2020

Please email any enquiries to kr2ml.ws@gmail.com

Best Paper Awards

Three best paper awards will selected, based on scientific merit, impact, and clarity. A $500.00 USD cash prize will be awarded to the 1st prize best paper. Best paper awards are nominated by program committee and judged by the Best Paper award committee.

Award sponsor: