more of Sugiton at dawn [jatp]

Sugiton at dawn [jatp]

data assimilation and reduced modelling for high-D problems [CIRM]

Next summer, from 19 July till 27 August, there will be a six week program at CIRM on the above theme, bringing together scientists from both the academic and industrial communities. The program includes a one-week summer school followed by 5 weeks of research sessions on projects proposed by academic and industrial partners.

Confirmed speakers of the summer school (Jul 19-23) are:

• Albert Cohen (Sorbonne University)
• Masoumeh Dashti (University of Sussex)
• Eric Moulines (Ecole Polytechnique)
• Anthony Nouy (Ecole Centrale de Nantes)
• Claudia Schillings (Mannheim University)

Junior participants may apply for fellowships to cover part or the whole stay. Registration and application to fellowships will be open soon.

end-to-end Bayesian learning [CIRM]

Next Fall, there will be a workshop at CIRM, Luminy, Marseilles, on Bayesian learning. It takes place 22-29 October 2021 on this wonderful campus at the border with the beautiful Parc National des Calanques, in a wonderfully renovated CIRM building and involves friends and colleagues of mine as organisers and plenary speakers. (I am not involved!, but plan to organise a scalable MCMC workshop there the year after!) The conference is well-supported and the housing fees will be minimal since the centre is also subsidized by CNRS. The deadline for contributed talks and posters is 22 March, while it is 15 June for registration. Hopefully by this time the horizon will have cleared up enough to consider traveling and meeting again. Hopefully. (In which case I will miss this wonderful conference due to other meeting and teaching commitments in the Fall.)

Big Bayes goes South

At the Big [Data] Bayes conference this week [which I found quite exciting despite a few last minute cancellations by speakers] there were a lot of clustering talks including the ones by Amy Herring (Duke), using a notion of centering that should soon appear on arXiv. By Peter Müller (UT, Austin) towards handling large datasets. Based on a predictive recursion that takes one value at a time, unsurprisingly similar to the update of Dirichlet process mixtures. (Inspired by a 1998 paper by Michael Newton and co-authors.) The recursion doubles in size at each observation, requiring culling of negligible components. Order matters? Links with Malsiner-Walli et al. (2017) mixtures of mixtures. Also talks by Antonio Lijoi and Igor Pruenster (Boconni Milano) on completely random measures that are used in creating clusters. And by Sylvia Frühwirth-Schnatter (WU Wien) on creating clusters for the Austrian labor market of the impact of company closure. And by Gregor Kastner (WU Wien) on multivariate factor stochastic models, with a video of a large covariance matrix evolving over time and catching economic crises. And by David Dunson (Duke) on distance clustering. Reflecting like myself on the definitely ill-defined nature of the [clustering] object. As the sample size increases, spurious clusters appear. (Which reminded me of a disagreement I had had with David McKay at an ICMS conference on mixtures twenty years ago.) Making me realise I missed the recent JASA paper by Miller and Dunson on that perspective.

Some further snapshots (with short comments visible by hovering on the picture) of a very high quality meeting [says one of the organisers!]. Following suggestions from several participants, it would be great to hold another meeting at CIRM in a near future.