AISTATS 2016 [post-submissions]

Now that the deadline for AISTATS 2016 submissions is past, I can gladly report that we got the amazing number of 559 submissions, which is much more than what was submitted to the previous AISTATS conferences. To the point it made us fear for a little while [but not any longer!] that the conference room was not large enough. And hope that we had to install video connections in the hotel bar!

Which also means handling about the same amount of papers as a year of JRSS B submissions within a single month!, the way those submissions are handled for the AISTATS 2016 conference proceedings. The process is indeed [as in other machine learning conferences] to allocate papers to associate editors [or meta-reviewers or area chairs] with a bunch of papers and then have those AEs allocate papers to reviewers, all this within a few days, as the reviews have to be returned to authors within a month, for November 16 to be precise. This sounds like a daunting task but it proceeded rather smoothly due to a high degree of automation (this is machine-learning, after all!) in processing those papers, thanks to (a) the immediate response to the large majority of AEs and reviewers involved, who bid on the papers that were of most interest to them, and (b) a computer program called the Toronto Paper Matching System, developed by Laurent Charlin and Richard Zemel. Which tremendously helps with managing about everything! Even when accounting for the more formatted entries in such proceedings (with an 8 page limit) and the call to the conference participants for reviewing other papers, I remain amazed at the resulting difference in the time scales for handling papers in the fields of statistics and machine-learning. (There was a short lived attempt to replicate this type of processing for the Annals of Statistics, if I remember well.)

AISTATS 2016 [call for submissions]

At the last (European) AISTATS 2014, I agreed to be the program co-chair for AISTATS 2016, along with Arthur Gretton from the Gatsby Unit, at UCL. (AISTATS stands for Artificial Intelligence and Statistics.) Thanks to Arthur’s efforts and dedication, as the organisation of an AISTATS meeting is far more complex than any conference I have organised so far!, the meeting is taking shape. First, it will take place in Cadiz, Andalucía, Spain, on May 9-11, 2016. (A place more related to the conference palm tree logo than the previous location in Reykjavik, even though I would be the last one to complain it took place in Iceland!)

Second, the call for submissions is now open. The process is similar to other machine learning conferences in that papers are first submitted for the conference proceedings, then undergo a severe and tight reviewing process, with a response period for the authors to respond to the reviewers’ comments, and that only the accepted papers can be presented as posters, some of which are selected for an additional oral presentation. The major dates for submitting to AISTATS 2016 are

Proceedings track paper submission deadline	23:59UTC Oct 9, 2015
Proceedings track initial reviews available	Nov 16, 2015
Proceedings track author feedback deadline	Nov 23, 2015
Proceedings track paper decision notifications	Dec 20, 2015

With submission instructions available at this address. Including the electronic submission site.

I was quite impressed by the quality and intensity of the AISTATS 2014 conference, which is why I accepted so readily being program co-chair, and hence predict an equally rewarding AISTATS 2016, thus encouraging all interested ‘Og’s readers to consider submitting a paper there! Even though I confess it will make a rather busy first semester for 2016, between MCMSki V in January, the CIRM Statistics month in February, the CRiSM workshop on Eatimating constants in April, AISTATS 2016 thus in May, and ISBA 2016 in June…

speeding up MCMC

Just before I left for Iceland, Matias Quiroz, Mattias Villani and Robert Kohn arXived a paper entitled “speeding up MCMC by efficient data subsampling”. Somewhat connected with the earlier papers by Koattikara et al., and Bardenet et al., both discussed on the ‘Og, the idea is to replace the log-likelihood by an unbiased subsampled version and to correct for the resulting bias of the exponentiation of this (Horwitz-Thompson or Hansen-Hurwitz) estimator. They ground their approach within the (currently cruising!) pseudo-marginal paradigm, even though their likelihood estimates are not completely unbiased. Since the optimal weights in the sampling step are proportional to the log-likelihood terms, they need to build a surrogate of the true likelihood, using either a Gaussian process or a spline approximation. This is all in all a very interesting contribution to the on-going debate about increasing MCMC speed when dealing with large datasets and ungainly likelihood functions. The proposed solution however has a major drawback in that the entire dataset must be stored at all times to ensure unbiasedness. For instance, the paper considers a bivariate probit model with a sample of 500,000 observations. Which must be available at all times.  Further, unless I am confused, the subsampling step requires computing the surrogate likelihood for all observations, before running the subsampling step, another costly requirement.

AISTATS 2014 (tee-shirt)

It took me a fairly long while to realise there was a map of Iceland as a tag-cloud at the back of the AISTATS 2014 tee-shirt! As it was far too large for me, I thought about leaving it at the conference desk last week. I did bring it back for someone the proper size though and discovered the above when unfolding the tee… Nice but still not my size!

skyndimynd frá Íslandi (#9)