Alas, thrice alas, the bid we made right after the Banff workshop with Scott Schmidler, and Steve Scott for holding the next World ISBA Conference in 2016 in Banff, Canada was unsuccessful. This is a sad and unforeseen item of news as we thought Banff had a heap of enticing features as a dream location for the next meeting… Although I cannot reveal the location of the winner, I can mention it is much more traditional (in the sense of the Valencia meetings), i.e. much more mare than monti… Since it is in addition organised by friends and in a country I love, I do not feel particularly aggravated. Especially when considering we will not have to organise anything then!
Archive for Canadian Rockies
Scott Schmidler, Steve Scott and myself just submitted a proposal for holding the next World ISBA Conference in 2016 in Banff, Canada! After enjoying the superb environment of the Advanced in Scalable Bayesian computation workshop last week, we thought it would be worth a try as a potential location for the next meeting, esp. when considering the superlative infrastructure of the Banff Centre (meaning we really do not have to be local to be local organisers!), the very reasonable rates for renting the site and securing two hundred rooms, the potential for a special collaboration with BIRS, the scarcity of alternative proposals (as far as I can fathom) and the ultimate mountain environment… I remember fondly the IMS annual meeting of 2002 there, with a great special lecture by Hans Künsch and, exceptionally, an RSS Read Paper by Steve Brooks, Paulo Guidici and Gareth Roberts. (Not mentioning en exhilarating solo scramble up Mount Temple and another one with Arnaud Guillin up the chimneys of Mount Edith!) Since the deadline was this Saturday, March 15, we should hear pretty soon if we are successful in this bid. (Good luck to our Scottish friends from Edinburgh for their bid for holding ISBA 2018! Moving from the feet of Mount Rundle [above] to the feet of Arthur’s Seat would make for a great transition.)
We have now gone over the midpoint of our workshop Advances in Scalable Bayesian Computation with three talks in the morning and an open research or open air afternoon. (Maybe surprisingly I chose to stay indoors and work on a new research topic rather than trying cross-country skiing!) If I must give a theme for the day, it would be (jokingly) corporate Big data, as the three speakers spoke of problems and solutions connected with Google, Facebook and similar companies. First, Russ Salakhutdinov presented some hierarchical structures on multimedia data, like connecting images and text, with obvious applications on Google. The first part described Boltzman machines with impressive posterior simulations of characters and images. (Check the video at 45:00.) Then Steve Scott gave us a Google motivated entry to embarrassingly parallel algorithms, along the lines of papers recently discussed on the ‘Og. (Too bad we forgot to start the video at the very beginning!) One of the novel things in the talk (for me) was the inclusion of BART in this framework, with the interesting feature that using the whole prior on each machine was way better than using a fraction of the prior, as predicted by the theory! And Joaquin Quinonero Candela provided examples of machine learning techniques used by Facebook to suggest friends and ads in a most efficient way (techniques remaining hidden!).
Even though the rest of the day was free, the two hours of exercising between the pool in the early morning and the climbing wall in the late afternoon left me with no energy to experiment curling with a large subsample of the conference attendees, much to my sorrow!
And here is the second day of our workshop Advances in Scalable Bayesian Computation gone! This time, it sounded like the “main” theme was about brains… In fact, Simon Barthelmé‘s research originated from neurosciences, while Dawn Woodard dissected a brain (via MRI) during her talk! (Note that the BIRS website currently posts Simon’s video as being Dan Simpson’s talk, the late change in schedule being due to Dan most unfortunately losing his passport during a plane transfer and most unfortunately being prevented from attending…) I found Simon’s talk quite inspiring, with this Tibshirani et al.’s trick of using logistic regression to estimate densities as a classification problem central to the method and suggesting a completely different vista for handling normalising constants… Then Raazesh Sainudiin gave a detailed explanation and validation of his approach to density estimation by multidimensional pavings/histograms, with a tree representation allowing for fast merging of different estimators. Raaz had given a preliminary version of the talk at CREST last Fall, which helped with focussing on the statistical aspects of the method. Chris Strickland then exposed an image analysis of flooded Northern Queensland landscapes, using a spatio-temporal model with changepoints and about 18,000 parameters. still managing to get an efficiency of O(np) thanks to two tricks. Then it was time for the group photograph outside in a balmy -18⁰ and an open research time that was quite profitable.
In the afternoon sessions, Paul Fearnhead presented an auxiliary variable approach to particle Gibbs, which again opened new possibilities for handling state-space models, but also reminding me of Xiao-Li Meng’s reparameterisation devices. And making me wonder (out loud) whether or not the SMC algorithm was that essential in a static setting, since the sequence could be explored in any possible order for a fixed time horizon. Then Emily Fox gave a 2-for-1 talk, mostly focussing on the first talk, where she introduced a new technique for approximating the gradient in Hamiltonian (or Hockey!) Monte Carlo, using second order Langevin. She did not have much time for the second talk, which intersected with the one she gave at BNP’ski in Chamonix, but focussed on a notion of sandwiched slice sampling where the target density only needs bounds that can get improved if needed. A cool trick! And the talks ended with Dawn Woodard‘s analysis of time varying 3-D brain images towards lesion detection, through an efficient estimation of a spatial mixture of normals.