Ten days ago I took part in the SEINE AI 2026 workshop in Jouy-en-Josas, near Paris (homestead of HEC), organised by the Huawei Paris Research Center.. In which I was invited to speak, even though I felt sort of an outlier given the deeply machine-learning, entreprenarial orientation of the meeting, with its theme being Building the Agentic Future of ICT, given that I chose to present our most recent Bayesian adversarial privacy paper. Hence, I stood within a game-theoretic, Bayesian, formal landscape, presumably loosing most of the audience and keeping them away from their lunch!
Other speakers included Simon Lucas from Queen Mary London on Simulation-based AI, which I had trouble distinguishing from building a statistical model by goodness of fit (and using bandits used for update), while focussing on competing on some computer game challenges. And Volker Tresp from LMU München on a tensor brain model that he opposes to a Bayesian brain (with a related paper entitled Bayes or Heisenberg: Who(se) rules? which we discussed in general terms over lunch, namely Bayesian learning vs. quantum updating. And Michal Valko from INRIA Paris (and other companies), who went full blast against the Bradley-Terry model!, with a title of Nash and Nemirovski walk into a bar! With a half-time technique approximating Nash equilibria that reminded me of leapfrog. Much entertaining talk that further provided a game-theoretic transition to mine’s.
As an aside, I played yesterday with ChatGPT composing my talk slides out of our arXiv document and it proved a disaster, with hallucinations of results and concepts not in the paper and a complete mess of handling graphs, first creating generic, fake, unrelated pictures, then inserting actual graphs haphazardly throughout the slides. The sorry result I obviously did not use as the workshop did not seem the ideal place for this sort of prank! The actual version only recycles a few of its summarising slides. (With ye Norse farce proper colour choice!)
The main BayesComp meeting started right after the ABC workshop and went on at a grueling pace, and offered a constant conundrum as to which of the four sessions to attend, the more when trying to enjoy some outdoor activity during the lunch breaks. My overall feeling is that it went on too fast, too quickly! Here are some quick and haphazard notes from some of the talks I attended, as for instance the practical parallelisation of an SMC algorithm by Adrien Corenflos, the advances made by Giacommo Zanella on using Bayesian asymptotics to assess robustness of Gibbs samplers to the dimension of the data (although with no assessment of the ensuing time requirements), a nice session on simulated annealing, from black holes to Alps (if the wrong mountain chain for Levi), and the central role of contrastive learning à la Geyer (1994) in the GAN talks of Veronika Rockova and Éric Moulines. Victor Elvira delivered an enthusiastic talk on our massively recycled importance on-going project that we need to complete asap!
In a not-solely-ABC session, I appreciated Sirio Legramanti speaking on comparing different distance measures via Rademacher complexity, highlighting that some distances are not robust, incl. for instance some (all?) Wasserstein distances that are not defined for heavy tailed distributions like the Cauchy distribution. And using the mean as a summary statistic in such heavy tail settings comes as an issue, since the distance between simulated and observed means does not decrease in variance with the sample size, with the practical difficulty that the problem is hard to detect on real (misspecified) data since the true distribution behing (if any) is unknown. Would that imply that only intrinsic distances like maximum mean discrepancy or Kolmogorov-Smirnov are the only reasonable choices in misspecified settings?! While, in the ABC session, Jeremiah went back to this role of distances for generalised Bayesian inference, replacing likelihood by scoring rule, and requirement for Monte Carlo approximation (but is approximating an approximation that a terrible thing?!). I also discussed briefly with Alejandra Avalos on her use of pseudo-likelihoods in Ising models, which, while not the original model, is nonetheless a model and therefore to taken as such rather than as approximation.
