oceanographers in Les Houches

Þe first internal research workshop of our ERC Synergy project OCEAN is taking place in Les Houches, French Alps, this coming week with 15 researchers gathering for brain-storming on some of the themes at the core of the project, like algorithmic tools for multiple decision-making agents, along with Bayesian uncertainty quantification and Bayesian learning under constraints (scarcity, fairness, privacy). Due to the small size of the workshop (which is perfect for engaging into joint work), it could not be housed by the nearby, iconic, École de Physique des Houches but will take place instead in a local hotel.

On the leisurely side, I hope there will be enough snow left for some lunch-time ski breaks [with no bone fracture à la Adapski!] Or, else, that the running trails nearby will prove manageable.

expectation-propagation from Les Houches

As CHANCE book editor, I received the other day from Oxford University Press acts from an École de Physique des Houches on Statistical Physics, Optimisation, Inference, and Message-Passing Algorithms that took place there in September 30 – October 11, 2013.  While it is mostly unrelated with Statistics, and since Igor Caron already reviewed the book a year and more ago, I skimmed through the few chapters connected to my interest, from Devavrat Shah’s chapter on graphical models and belief propagation, to Andrea Montanari‘s denoising and sparse regression, including LASSO, and only read in some detail Manfred Opper’s expectation propagation chapter. This paper made me realise (or re-realise as I had presumably forgotten an earlier explanation!) that expectation propagation can be seen as a sort of variational approximation that produces by a sequence of iterations the distribution within a certain parametric (exponential) family that is the closest to the distribution of interest. By writing the Kullback-Leibler divergence the opposite way from the usual variational approximation, the solution equates the expectation of the natural sufficient statistic under both models… Another interesting aspect of this chapter is the connection with estimating normalising constants. (I noticed a slight typo on p.269 in the final form of the Kullback approximation q() to p().