Herr Strathmann.

I Like Intractable Likelihoods

Last week, I went to the i-like workshop at Oxford university. Pretty cool! All of Britain's statisticians were there and I met many of them for the first time. Check out my two posters (Russian Roulette, Kernel Adaptive Metropolis Hastings). Talks were amazing - as in last NIPS, the main trend is on estimating likelihoods (well, that's the name of the program), either using some other random process such as importance sampling a latent model's marginal likelihood (aka Pseudo-Marginal MCMC), or directly sub-sampling likelihoods or gradients.

These things are important in Machine Learning too, and it is very nice to see the field growing together (even-though there was a talk by a Statistician spending lots of time on re-inventing belief propagation and Junction tree ideas - always such a pitty if this happens simply because communities do not talk to each other enough). Three talks that I really found interesting:

Remi Bardenet talked about sub-sampling approaches to speed up MCMC. This is quite related to the Austerity in MCMC land paper by Welling & Co, with the difference that his tests do not suffer from small number of points in the hypothesis test to decide accept/reject.

Chris Sherlock talked about optimal rates and scaling for Pseudo-Marginal MCMC. There finally are some nice heuristics how to scale PM estimates in a way that the number of iid samples per computation time is optimal. Interestingly, the acceptance rate and the variance of the likelihood estimate can be tweaked separately.

Jim Griffin gave a very interesting talk on adaptive MCMC on discrete, in particular binary, state-spaces - he used them for feature selection (in ML language). His algorithm automatically learns global mutations rates for each of the positions. However, it doesn't take any correlations between the features into account. This might be a very interesting application for our fancy Kameleon sampler (arxiv, code), thinking about this!

Finally, I presented two posters, the one on Playing Russian Roulette with Intractable Likelihoods that I already presented in Reykjavik, and (with Dino) a new poster (link) on the Kernel Adaptive Metropolis Hastings Kameleon that I mentioned above. The corresponding paper is hopefully published very soon. Talking to other scientists about my own work is just great!