AISTATS 2016 [#2]

The second and third days of AISTATS 2016 passed like a blur, with not even the opportunity to write my impressions in real time! Maybe long tapa breaks are mostly to blame for this… In any case, we had two further exciting plenary talks about privacy-preserving data analysis by Kamalika Chaudhuri and crowdsourcing and machine learning by Adam Tauman Kalai. The talk by Kamalika was covering recent results by Kamalika and coauthors about optimal privacy preservation in classification and a generalisation to correlated data, with the neat notion of a Markov Quilt.  Other talks that same day also dwelt on this privacy issue, but I could not be . The talk by Adam was full of fun illustrations on humans training learning systems (with the unsolved difficulty of those humans deliberately mis-training the system, as exhibited recently by the short-lived Microsoft Tay experiment).

Both poster sessions were equally exciting, with the addition of MLSS student posters on the final day. Among many, I particularly enjoyed Iain Murray’s pseudo-marginal slice sampling, David Duvenaud’s fairly intriguing use of early stopping for non-parametric inference,  Garrett Bernstein’s work on aggregated Markov chains, Ye Wang’s scalable geometric density estimation [with a special bonus for his typo on the University of Turing, instead of Torino], Gemma Moran’s and Chengtao Li’s posters on determinantal processes, and Matej Balog’s Mondrian forests with a Laplace kernel [envisioning potential applications for ABC]. Again, just to mention a few…

The participants [incl. myself] also took one evening off to visit a sherry winery in Jerez, with a well-practiced spiel on the story of the company, with some building designed by Gutave Eiffel, and with a wine-tasting session. As I personally find this type of brandy too strong in alcohol, I am not a big fan of sherry but it was nonetheless an amusing trip! With no visible after-effects the next morning, since the audience was as large as usual for Adam’s talk [although I did not cross a machine-learning soul on my 6am run…]

In short, I enjoyed very much AISTATS 2016 and remain deeply impressed by the efficiency of the selection process and the amount of involvement of the actors of this selection, as mentioned earlier on the ‘Og. Kudos!

AISTATS 2016 [#1]

Travelling through Seville, I arrived in Càdiz on Sunday night, along with a massive depression [weather-speaking!]. Walking through the city from the station was nonetheless pleasant as this is an town full of small streets and nice houses. If with less churches than Seville! Richard Samworth gave the first plenary talk of AISTATS 2016  with a presentation on random projections for classification. His classifier is based on an average of a large number of linear random projections of the original data where the projections are chosen as minimising the prediction error over a subset of the components. The performances of this approach seem to be consistently higher than for random forests, which makes it definitely worth investigating further. (A related R package is available.)

The following talks that day covered Bayesian optimisation and probabilistic numerics, with Javier Gonzales introducing glasses for Bayesian optimisation in order to solve its myopia (!)—by which he meant predicting the output of the optimisation over n future steps. And a first mention of the Pima Indians by Daniel Hernandez-Lobato in his talk about EP with stochastic gradient steps towards optimisation. (As well as much larger datasets.) And Mark Girolami bringing quasi-Monte Carlo into control variates. A kernel based ABC by Mijung Park, which uses kernels and maximum mean discrepancy to avoid defining summary statistics, and a version of parallel MCMC by Guillaume Basse. Plus another session on deep learning.

As usual with AISTATS conferences, the central activity of the day was the noon poster session, including speakers discussing their paper, and I had several interesting chats about MCMC related topics, with e.g. one alternative notion of ensemble MCMC [centred on estimating the normalising constant].

We awarded the notable student paper awards before the welcoming cocktail: The winners are Bo Dai, Nedelina Teneva, and Ye Wang.  And this first day ended up with a companionable evening in a most genuine tapa bar, tasting local blood sausage and local blue cheese. (If you do not mind the corrida theme!)