## Symposium on Advances in Approximate Bayesian Inference

December 2, 2018

We invite researchers in machine learning and statistics to participate in the I Symposium on Advances in Approximate Bayesian Inference.

### Key Dates

• Paper submission: 19 October 2018 (11:55pm GMT)
• Acceptance notification: 13 November 2018
• Final paper submission: 1 December 2018

### Submission Details

We invite researchers to submit their recent work on the development, analysis, or application of approximate Bayesian inference. A submission should take the form of an extended abstract of 2-4 pages in PDF format using the PMLR one-column style (template). For questions and troubleshooting, visit CTAN. Author names do not need to be anonymized and references may extend as far as needed beyond the 4 page upper limit. If authors' research has previously appeared in a journal, workshop, or conference (including the NIPS 2018 conference), their symposium submission should extend that previous work. Submissions may include a supplement/appendix, but reviewers are not responsible for reading any supplementary material.

All submissions will be reviewed by at least three reviewers from the field. Accepted submissions will be accepted to presentation only. The authors of selected submissions will be invited to publish their paper in a PMLR volume. We aim to keep a general inclusive nature of the symposium for presentations. However, we will only invite the top-rated accepted papers to be published through PMLR.

Please submit here. Submissions will be accepted as poster presentations. Selected submissions will also be considered for awards and contributed talks. Final versions of the symposium submissions are due by 1 December, and will be posted on the symposium website.

Submit final versions of papers by updating the file on EasyChair. Posters should not exceed the A1 paper size in vertical format (approximately 24 inches width by 33 inches height).

Thanks to our sponsors, we will provide travel awards between $2000 and$3000 in total, to be allocated across winners.