Archive for probabilistic programming

NIPS 2014

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on December 15, 2014 by xi'an

mugSecond and last day of the NIPS workshops! The collection of topics was quite broad and would have made my choosing an ordeal, except that I was invited to give a talk at the probabilistic programming workshop, solving my dilemma… The first talk by Kathleen Fisher was quite enjoyable in that it gave a conceptual discussion of the motivations for probabilistic languages, drawing an analogy with the early days of computer programming that saw a separation between higher level computer languages and machine programming, with a compiler interface. And calling for a similar separation between the models faced by statistical inference and machine-learning and the corresponding code, if I understood her correctly. This was connected with Frank Wood’s talk of the previous day where he illustrated the concept through a generation of computer codes to approximately generate from standard distributions like Normal or Poisson. Approximately as in ABC, which is why the organisers invited me to talk in this session. However, I was a wee bit lost in the following talks and presumably lost part of my audience during my talk, as I realised later to my dismay when someone told me he had not perceived the distinction between the trees in the random forest procedure and the phylogenetic trees in the population genetic application. Still, while it had for me a sort of Twilight Zone feeling of having stepped in another dimension, attending this workshop was an worthwhile experiment as an eye-opener into a highly different albeit connected field, where code and simulator may take the place of a likelihood function… To the point of defining Hamiltonian Monte Carlo directly on the former, as Vikash Mansinghka showed me at the break.

I completed the day with the final talks in the variational inference workshop, if only to get back on firmer ground! Apart from attending my third talk by Vikash in the conference (but on a completely different topic on variational approximations for discrete particle-ar distributions), a talk by Tim Salimans linked MCMC and variational approximations, using MCMC and HMC to derive variational bounds. (He did not expand on the opposite use of variational approximations to build better proposals.) Overall, I found these two days and my first NIPS conference quite exciting, if somewhat overpowering, with a different atmosphere and a different pace compared with (small or large) statistical meetings. (And a staggering gender imbalance!)

AISTATS 2014 [day #3]

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , on April 28, 2014 by xi'an

IMG_0574The 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.

The last session of the day and of the conference was on more statistical issues, like a Gaussian process modelling of aKeflavik2 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!

The 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).