van der Schaar Lab

Call for Papers

We invite submissions of 4-6 pages (references not included) of original papers on the challenges and advances in synthetic data.

Example topics include (but are not limited to):

  • Privacy-preserving synthetic data: theory, algorithm, attack modes, etc
  • Generation of out-of-distribution data, cross-domain and multi-modal generation for boosting ML models
  • Synthetic data and data augmentation for fairness
  • Synthetic data for specific domains and tasks: architecture, inductive biases, etc
  • Synthetic data evaluation and performance metrics: quality, utility, privacy, etc
  • Using large language models to generate synthetic data for various downstream tasks

Submission may include an appendix with unlimited pages after the main text and additional supplementary material of maximum 50 MB in zip format (e.g. code), but reviewers will not be obliged to read these materials. The authors should use the NeurIPS style file provided here. All submissions are expected to conform to the NeurIPS code of conduct.

Submissions solely based on work previously published in machine learning conferences or relevant venues are unsuitable for the workshop. On the other hand, we allow submission of works currently under submission and relevant works recently published in relevant venues. The workshop will not have formal proceedings, but authors of accepted abstracts can choose to have a link to Arxiv or a pdf added to the workshop webpage.

Important Dates


The review process is double-blind, and the submission must be anonymised.

NOTE: the submissions will only be visible to the assigned reviewers, area chairs, and the authors themselves during the reviewing phase. Accepted papers will be made public on OpenReview after the reviewing phase. Withdrawn papers will never be made public.


All accepted papers will be downloadable from the workshop webpage and are expected to be presented as posters during the workshop. However, the workshop does not publish formal proceedings and the acceptance is non-archival. This means that authors are free to publish their work in archival journals or at conferences.