Archive for Reykjanes Peninsula

desperately seeking puffins!

Posted in Kids, Mountains, pictures, Travel with tags , , , , , , , on June 13, 2015 by xi'an

cliffOn Sunday afternoon, I made a brief trip to the southern coast of the Reykjanes Peninsula in an attempt to watch puffins. According to my guide book, the cliffs at Krýsuvíkurberg were populated with many species of birdlife, including the elusive puffin. However, I could only spot gulls, and more gulls, as I walked a few kilometres along those cliffs and away from the occasional 4WDcliff2 stopping by the end of a dirt road [my small rental car could not handle that far]. When I was about to turn back, I spotted different birds on a small rock promontory, too far for me to tell the species, and as I was zooming at them, a puffin flew by!, so small that I almost missed it. I tried to see if any other was dwelling in the cliffs left and right but to no avail. A few minutes later, presumably the same puffin flew back and this was the end of it. Even after looking at the enlarged picture, I cannot tell what those “other” birds are: presumably Brünnich’s guillemots…

Icelandic landscape [#2]

Posted in Mountains, pictures, Travel with tags , , , , , , , on June 10, 2015 by xi'an

Kleifarvatn2

moon under the midnight sun

Posted in Mountains, pictures, Travel, University life with tags , , , , , on June 6, 2015 by xi'an

moon

skyndimynd frá Íslandi (#9)

Posted in Kids, Mountains, pictures, Travel with tags , , , , on May 5, 2014 by xi'an

puffin

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).