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”

keep meetings hybrid

I was reading the latest ISBA Bulletin and the tribune by ISBA President Sudipto Banerjee celebrating the return to the physical ISBA World meeting, along with worries about participants who caught COVID there. (Unfortunately, one good friend of mine experienced symptoms that went beyond the mild cold-like ones I zoomed through a few days ago.) This particular issue of creating a COVID cluster [during coffee breaks?!] provides [me with] one further argument for my supporting hybrid and multimodal meetings on a general basis. Which should [imho] appear in the proposals for the 2026 and 2028 World Meetings (deadline on 31 October)…(The 2024 meeting in Venezia will certainly involve hybridicity! As will BayesComp in Levi.) Discussing the topic with others in some scientific committees recently made me realise this was not such a shared perspective, from reasons varying from worrying about balancing the budget, to zoom fatigue, to the added value of informal interactions. Still, there also are reasons for hybridising our meetings, from reduced travel impact, to more inclusiveness,  on geographical, diversity, affordability, seniority grounds. Holding hybrid conferences with multiple regional mirrors allows for a potentially higher degree of interaction and local input.  And a minimal organisational effort.

ABC in… everywhere [programme]

The ABC in Svalbard workshop is taking place on-line next week (and most sadly not in Svalbard). The programme is available on the ABC site. It starts (in Australia) at 4:00GMT (14 AEST) and finishes (in France) at 15:30GMT (17:30 CET). Registration is free but needed to access the Zoom codes!  See you on Zoom next week!!!

missing bit?

Nature of 7 December 2020 has a Nature Index (a supplement made of a series of articles, more journalistic than scientific, with corporate backup, which “have no influence over the content”) on Artificial Intelligence, including the above graph representing “the top 200 collaborations among 146 institutions based between 2015 and 2019, sized according to each institution’s share in artificial intelligence”, with only the UK, Germany, Switzerland and Italy identified for Europe… Missing e.g. the output from France and from its major computer science institute, INRIA. Maybe because “the articles picked up by [their] database search concern specific applications of AI in the life sciences, physical sciences, chemistry, and Earth and environmental sciences”.  Or maybe because of the identification of INRIA as such.

“Access to massive data sets on which to train machine-learning systems is one advantage that both the US and China have. Europe, on the other hand, has stringent data laws, which protect people’s privacy, but limit its resources for training AI algorithms. So, it seems unlikely that Europe will produce very sophisticated AI as a consequence”

This comment is sort of contradictory for the attached articles calling for a more ethical AI. Like making AI more transparent and robust. While having unrestricted access to personal is helping with social engineering and control favoured by dictatures and corporate behemoths, a culture of data privacy may (and should) lead to develop new methodology to work with protected data (as in an Alan Turing Institute project) and to infuse more trust from the public. Working with less data does not mean less sophistication in handling it but on the opposite! Another clash of events appears in one of the six trailblazers portrayed in the special supplement being Timnit Gebru, “former co-lead of the Ethical AI Team at Google”, who parted way with Google at the time the issue was published. (See Andrew’s blog for Jessica Hullman discussion of her firing. And the MIT Technology Review for an analysis of the paper potentially at the source of it.)

Francis Bach à l’Académie des Sciences

Congrats to Francis Bach, freshly nominated to the French Academy of Sciences, joining Stéphane Mallat²⁰¹⁴ and Éric Moulines²⁰¹⁷ as data science academicians!