Archive for ICLR

[Split] Frontiers in Statistical Machine Learning [reposted]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on September 19, 2026 by xi'an

In connection with the IMS conference ICSDS 2026, an IMS Frontiers in Statistical Machine Learning (FSML) satellite workshop takes place on Monday, December 14, 2026 (also) in Split, Croatia, the day before the main conference.

This year’s themes are generative and foundation models for statistics, and the science of deep learning. The keynote speakers are Yuxin Chen, Alexander Henzi, Andrej Risteski, Pragya Sur, Yan Shuo Tan, and Yuexi Wang.

There are two ways to present a poster, both non-archival:

– Workshop Track: short papers of 3 to 5 pages, work in progress welcome. Ten US$500 travel awards for students and postdocs.
– Fast Track: papers already accepted at NeurIPS, ICLR, AISTATS, ICML, UAI, JMLR, or TMLR since August 2025. No additional review.

The deadline for both tracks is Monday, October 19

FSML 2026 organizers are:
Yuansi Chen, ETH Zurich
Sophie Langer, Ruhr University Bochum
Feng Liu, University of Melbourne
Xinwei Shen, University of Washington
Susan Wei, Monash University

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open reviews

Posted in Statistics with tags , , , , , , on September 13, 2019 by xi'an

When looking at a question on X validated, on the expected Metropolis-Hastings ratio being one (not all the time!), I was somewhat bemused at the OP linking to an anonymised paper under review for ICLR, as I thought this was breaching standard confidentiality rules for reviews. Digging a wee bit deeper, I realised this was a paper from the previous ICLR conference, already published both on arXiv and in the 2018 conference proceedings, and that ICLR was actually resorting to an open review policy where both papers and reviews were available and even better where anyone could comment on the paper while it was under review. And after. Which I think is a great idea, the worst possible situation being a poor paper remaining un-discussed. While I am not a big fan of the brutalist approach of many machine-learning conferences, where the restrictive format of both submissions and reviews is essentially preventing in-depth reviews, this feature should be added to statistics journal webpages (until PCIs become the norm).

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