**T**he third day at AISTATS 2014 started with Michael Jordan giving his plenary lecture, or rather three short talks on “Big Data” privacy, communication risk, and (bag of) bootstrap. I had not previously heard Michael talking about the first two topics and further found interesting the attempt to put computation into the picture (a favourite notion of Michael’s), however I was a bit surprised at the choice of a minimax criterion. Indeed, getting away from the minimax criterion was one of the major reasons I move to the B side of the Force. Because it puts exactly the same importance on every single value of the parameter. Even the most impossible ones. I was also a wee bit surprised at the optimal solution produced by this criterion: in a multivariate binary data setting (e.g., multiple drugs usage), the optimal privacy solution was to create a random binary vector and pick at random between this vector and its complement, depending on which one is closest to the observable. The loss of information seems formidable if the dimension of the vector is large. (Implementing ABC as a privacy [privacizing?] strategy would sound better if less optimal…) The next session was about *deep learning*, of which I knew [and know nothing], but the talk by Yoshua Bengio raised very relevant questions, like how to learn where the main part of the mass of a probability distribution is, besides pointing at a recent survey of his’. The survey points at some notions that I master and some that I don’t, but a cursory reading does not lead me to put an intuitive meaning on deep learning.

**T**he last session of the day and of the conference was on more statistical issues, like a Gaussian process modelling of a spatio-temporal dataset on Afghanistan attacks by Guido Sanguinetti, the use of Rao-Blackwellisation and control variate to build black-box variational inference by Rajesh Ranganath, the construction of conditional exponential families on mixed graphs by Pradeep Ravikumar, and a presentation of probabilistic programming with Anglican by Frank Wood that I had already seen in Banff. In particular, I found the result on the existence of joint exponential families on graphs when defined by those full conditionals quite exciting!

**T**he second poster session was in the early evening, with many more posters (and plenty of food and drinks!), as it also included the (non-refereed) MLSS posters. Among the many interesting ones I spotted, a way to hit-and-run for quasi-concave densities, estimating mixtures with negative weights, a failing particle algorithm for a flu epidemics, an exact EP algorithm, and a fairly intense discussion around Richard Wilkinson’s poster on Gaussian process ABC algorithm (that I discussed on the ‘Og a while ago).