Archive for UAI

[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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latent variables for a hierarchical Poisson model

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , on March 11, 2021 by xi'an

Answering a question on X validated about a rather standard hierarchical Poisson model, and its posterior Gibbs simulation, where observations are (d and w being a document and a word index, resp.)

N_{w,d}\sim\mathcal P(\textstyle\sum_{1\le k\le K} \pi_{k,d}\varphi_{k,w})\qquad(1)

I found myself dragged into an extended discussion on the validation of creating independent Poisson latent variables

N_{k,w,d}\sim\mathcal P(\pi_{k,d}\varphi_{k,w})\qquad(2)

since observing their sum in (1) was preventing the latent variables (2) from being independent. And then found out that the originator of the question had asked on X validated an unanswered and much more detailed question in 2016, even though the notations differ. The question does contain the solution I proposed above, including the Multinomial distribution on the Poisson latent variables given their sum (and the true parameters). As it should be since the derivation was done in a linked 2014 paper by Gopalan, Hofman, and Blei, later published in the Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI). I am thus bemused at the question resurfacing five years later in a much simplified version, but still exhibiting the same difficulty with the conditioning principles…

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