simulation as optimization [by kernel gradient descent]

Yesterday, which proved an unseasonal bright, warm, day, I biked (with a new wheel!) to the east of Paris—in the Gare de Lyon district where I lived for three years in the 1980’s—to attend a Mokaplan seminar at INRIA Paris, where Anna Korba (CREST, to which I am also affiliated) talked about sampling through optimization of discrepancies.
This proved a most formative hour as I had not seen this perspective earlier (or possibly had forgotten about it). Except through some of the talks at the Flatiron Institute on Transport, Diffusions, and Sampling last year. Incl. Marilou Gabrié’s and Arnaud Doucet’s.
The concept behind remains attractive to me, at least conceptually, since it consists in approximating the target distribution, known up to a constant (a setting I have always felt standard simulation techniques was not exploiting to the maximum) or through a sample (a setting less convincing since the sample from the target is already there), via a sequence of (particle approximated) distributions when using the discrepancy between the current distribution and the target or gradient thereof to move the particles. (With no randomness in the Kernel Stein Discrepancy Descent algorithm.)
Ana Korba spoke about practically running the algorithm, as well as about convexity properties and some convergence results (with mixed performances for the Stein kernel, as opposed to SVGD). I remain definitely curious about the method like the (ergodic) distribution of the endpoints, the actual gain against an MCMC sample when accounting for computing time, the improvement above the empirical distribution when using a sample from π and its ecdf as the substitute for π, and the meaning of an error estimation in this context.

“exponential convergence (of the KL) for the SVGD gradient flow does not hold whenever π has exponential tails and the derivatives of ∇ log π and k grow at most at a polynomial rate”

sequential meetings in Edinburgh

There will be not one but two consecutive events in Edinburgh next May²⁴ on sequential Monte Carlo methods! Both hosted by the fantastic International Centre for Mathematical Sciences (ICMS) in Edinburgh Olde Town. Within the Bayes Centre. And running distance to Arthur’s Seat. (Reminding me of my first ICMS workshop in 2001 run with Mike Titterington. May have been my first week long visit to Edinburgh as well…)

First, a Summer School on Bayesian filtering: fundamental theory and numerical methods (SSBF 2024), Edinburgh (UK), May 6-10, 2024. This summer (in the Scottish sense!) school will cover topics related to fundamental theory, state-of-the-art methodologies, and real-world applications.

Second, a Sequential Monte Carlo workshop (SMC 2024), the week later, on May 13-17, 2024. The workshop will cover topics related to sequential Monte Carlo and nearby fields, from theory to applications, following earlier workshops in the series. Including the one at CREST in 2015.

Thanks to Víctor Elvira, Jana de Wiljes, and Dan Crisan for this double deal (and the opportunity to return to Scotland for the first time since the pandemic).

to be demolished!

SMC 22 coming soon!

The 5th Workshop on Sequential Monte Carlo Methods (SMC 2022) will take place in Madrid on 4-6 May 2022. More precisely on the Leganés campus of Universidad Carlos III de Madrid. Registrations are now open, with very modest registration fees and the list of invited speakers is available on the webpage of the workshop. (The SMC 2020 workshop was cancelled due to the COVID-19 pandemic. An earlier workshop took place at CREST in 2015.)

David Cox (1924-2022)

It is with much sadness that I heard from Oxford yesterday night that David Cox had passed away. Hither goes a giant of the field, whose contributions to theoretical and methodological statistics are enormous and whose impact on society is truly exceptional. He was the first recipient of the International Prize in Statistics in 2016 (aka the “Nobel of Statistics”) among many awards and a Fellow of the Royal Society among many other recognitions. He was also the editor of Biometrika for 25 years (!) and was still submitting papers to the journal a few month ago. Statistical Science published a conversation between Nancy Reid and him that tells a lot about the man and his amazing modesty. While I had met him in 1989, when he was visiting Cornell University as a distinguished visitor (and when I drove him to the house of Anne and George Casella for dinner once), then again in the 1990s when he came on a two-day visit to CREST,  we only really had a significant conversation in 2011 (!), when David and I attended the colloquium in honour of Mike Titterington in Glasgow and he proved to be most interested in the ABC algorithm. He published a connected paper in Biometrika the year after, with Christiana Katsonaki. We met a few more times later, always in Oxford, to again discuss ABC. In each occasion, he was incredibly kind and considerate.