Archive for NeurIPS

neurips unconference [ELLIS]

Posted in Statistics, Travel, University life with tags , , , , , , , , , on October 2, 2026 by xi'an

[Reposting:] The ELLIS UnConference is an annual community gathering, bringing together the European machine learning community to foster exchange, collaboration, and scientific discussion. Following several successful editions in Copenhagen, Spain, Germany and France, the event returns this year as a one-day gathering kicking off the NeurIPS satellite in Paris. Join us for a day of community building, scientific exchange, and the opportunity to present a paper recently accepted at a top machine learning venue. Registration for the event and for the poster session takes place through a single form.

[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

Shane MacGowan (1957-2023)

Posted in Kids, Wines with tags , , , , , , , , , , , , , on December 4, 2023 by xi'an

The punk singer Shane MacGowan has died a few days ago. He formed the fantastic Pogues Celtic punk band in the early 1980’s, mixing punk raw energy with Celtic tunes and instruments, and Irish nationalism. (In true punk fashion, the name of the band come from the Gaelic póg mo thóin that I let readers check for translation! In the same spirit, his earlier band was called the Nips with no connection with the Neural Information Processing Systems conference! However, he could have claimed a connection with Bayes since he was raised in Tunbridge Wells.) His early death is sadly unsurprising given his lifelong issue with alcohol, which started as a young boy when he started drinking Guinness… (As a minor trivia, the Dirty Old Town song was originally written about Salford, near Manchester, England, although adopted by several Irish bnds.)

ISBA 2021 grand finale

Posted in Kids, Mountains, pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on July 3, 2021 by xi'an

Last day of ISBA (and ISB@CIRM), or maybe half-day, since there are only five groups of sessions we can attend in Mediterranean time.

My first session was one on priors for mixtures, with 162⁺ attendees at 5:15am! (well, at 11:15 Wien or Marseille time), Gertrud Malsiner-Walli distinguishing between priors on number of components [in the model] vs number of clusters [in the data], with a minor question of mine whether or not a “prior” is appropriate for a data-dependent quantity. And Deborah Dunkel presenting [very early in the US!] anchor models for fighting label switching, which reminded me of the talk she gave at the mixture session of JSM 2018 in Vancouver. (With extensions to consistency and mixtures of regression.) And Clara Grazian debating on objective priors for the number of components in a mixture [in the Sydney evening], using loss functions to build these. Overall it seems there were many talks on mixtures and clustering this year.

After the lunch break, when several ISB@CIRM were about to leave, we ran the Objective Bayes contributed session, which actually included several Stein-like minimaxity talks. Plus one by Théo Moins from the patio of CIRM, with ciccadas in the background. Incredibly chaired by my friend Gonzalo, who had a question at the ready for each and every speaker! And then the Savage Awards II session. Which ceremony is postponed till Montréal next year. And which nominees are uniformly impressive!!! The winner will only be announced in September, via the ISBA Bulletin. Missing the ISBA general assembly for a dinner in Cassis. And being back for the Bayesian optimisation session.

I would have expected more talks at the boundary of BS & ML (as well as COVID and epidemic decision making), the dearth of which should be a cause for concern if researchers at this boundary do not prioritise ISBA meetings over more generic meetings like NeurIPS… (An exception was George Papamakarios’ talk on variational autoencoders in the Savage Awards II session.)

Many many thanks to the group of students at UConn involved in setting most of the Whova site and running the support throughout the conference. It indeed went on very smoothly and provided a worthwhile substitute for the 100% on-site version. Actually, I both hope for the COVID pandemic (or at least the restrictions attached to it) to abate and for the hybrid structure of meetings to stay, along with the multiplication of mirror workshops. Being together is essential to the DNA of conferences, but travelling to a single location is not so desirable, for many reasons. Looking for ISBA 2022, a year from now, either in Montréal, Québec, or in one of the mirror sites!

Nature tidbits [the Bayesian brain]

Posted in Statistics with tags , , , , , , , , , , , , , , on March 8, 2020 by xi'an

In the latest Nature issue, a long cover of Asimov’s contributions to science and rationality. And a five page article on the dopamine reward in the brain seen as a probability distribution, seen as distributional reinforcement learning by researchers from DeepMind, UCL, and Harvard. Going as far as “testing” for this theory with a p-value of 0.008..! Which could be as well a signal of variability between neurons to dopamine rewards (with a p-value of 10⁻¹⁴, whatever that means). Another article about deep learning about protein (3D) structure prediction. And another one about learning neural networks via specially designed devices called memristors. And yet another one on West Africa population genetics based on four individuals from the Stone to Metal age (8000 and 3000 years ago), SNPs, PCA, and admixtures. With no ABC mentioned (I no longer have access to the journal, having missed renewal time for my subscription!). And the literal plague of a locust invasion in Eastern Africa. Making me wonder anew as to why proteins could not be recovered from the swarms of locust to partly compensate for the damages. (Locusts eat their bodyweight in food every day.) And the latest news from NeurIPS about diversity and inclusion. And ethics, as in checking for responsibility and societal consequences of research papers. Reviewing the maths of a submitted paper or the reproducibility of an experiment is already challenging at times, but evaluating the biases in massive proprietary datasets or the long-term societal impact of a classification algorithm may prove beyond the realistic.