an attempt at bloggin, nothing more…

**T**he paper ‘Unbiased Markov chain Monte Carlo methods with couplings’ by Pierre Jacob et al. will be discussed (or Read) tomorrow at the Royal Statistical Society, 12 Errol Street, London, tomorrow night, Wed 11 December, at 5pm London time. With a pre-discussion session at 3pm, involving Chris Sherlock and Pierre Jacob, and chaired by Ioanna Manolopoulou. While I will alas miss this opportunity, due to my trip to Vancouver over the weekend, it is great that that the young tradition of pre-discussion sessions has been rekindled as it helps put the paper into perspective for a wider audience and thus makes the more formal Read Paper session more profitable. As we discussed the paper in Paris Dauphine with our graduate students a few weeks ago, we will for certain send one or several written discussions to Series B!

**T**oday’s Advances in Approximate Bayesian Inference symposium, organised by Thang Bui, Adji Bousso Dieng, Dawen Liang, Francisco Ruiz, and Cheng Zhang, took place in front of Vancouver Harbour (and the tentalising ski slope at the back) and saw more than 400 participants, drifting away from the earlier versions which had a stronger dose of ABC and much fewer participants. There were students’ talks in a fair proportion, as well (and a massive number of posters). As of below, I took some notes during some of the talks with no pretense at exhaustivity, objectivity or accuracy. (This is a blog post, remember?!) Overall I found the day exciting (to the point I did not suffer at all from the usal naps consecutive to very short nights!) and engaging, with a lot of notions and methods I had never heard about. (Which shows how much I know nothing!)

The first talk was by Michalis Titsias, *Gradient-based Adaptive Markov Chain Monte Carlo* (jointly with Petros Dellaportas) involving as its objective function the multiplication of the variance of the move and of the acceptance probability, with a proposed adaptive version merging gradients, variational Bayes, neurons, and two levels of calibration parameters. The method advocates using this construction in a burnin phase rather than continuously, hence does not require advanced Markov tools for convergence assessment. (I found myself less excited by adaptation than earlier, maybe because it seems like switching one convergence problem for another, with additional design choices to be made.)The second talk was by Jakub Swiatkowsk, *The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks*, involving mean field approximation in variational inference (loads of VI at this symposium!), meaning *de facto* searching for a MAP estimator, and reminding me of older factor analysis and other *analyse de données* projection methods, except it also involved neural networks (what else at NeurIPS?!)The third talk was by Michael Gutmann, *Robust Optimisation Monte Carlo*, (OMC) for implicit data generated models (Diggle & Graton, 1982), an ABC talk at last!, using a formalisation through the functional representation of the generative process and involving derivatives of the summary statistic against parameter, in that sense, with the (Bayesian) random nature of the parameter sample only induced by the (frequentist) randomness in the generative transform since a new parameter “realisation” is obtained there as the one providing minimal distance between data and pseudo-data, with no uncertainty or impact of the prior. The Jacobian of this summary transform (and once again a neural network is used to construct the summary) appears in the importance weight, leading to OMC being unstable, beyond failing to reproduce the variability expressed by the regular posterior or even the ABC posterior. It took me a while to wonder `where is Wally?!’ (the prior) as it only appears in the importance weight.

The fourth talk was by Sergey Levine, *Reinforcement Learning, Optimal , Control, and Probabilistic Inference*, back to Kullback-Leibler as the objective function, with linkage to optimal control (with distributions as actions?), plus again variational inference, producing an approximation in sequential settings. This sounded like a type of return of the MaxEnt prior, but the talk pace was so intense that I could not follow where the innovations stood.

The fifth talk was by Iuliia Molchanova, on *Structured Semi-Implicit Variational Inference*, from BAyesgroup.ru (I did not know of a Bayesian group in Russia!, as I was under the impression that Bayesian statistics were under-represented there, but apparently the situation is quite different in machine learning.) The talk brought an interesting concept of semi-implicit variational inference, exploiting some form of latent variables as far as I can understand, using mixtures of Gaussians.

The sixth talk was by Rianne van den Berg, *Normalizing Flows for Discrete Data,* and amounted to covering three papers also discussed in NeurIPS 2019 proper, which I found somewhat of a suboptimal approach to an invited talk, as it turned into a teaser for following talks or posters. But the teasers it contained were quite interesting as they covered normalising flows as integer valued controlled changes of variables using neural networks about which I had just became aware during the poster session, in connection with papers of Papamakarios et al., which I need to soon read.

The seventh talk was by Matthew Hoffman: *Langevin Dynamics as Nonparametric Variational Inference*, and sounded most interesting, both from title and later reports, as it was bridging Langevin with VI, but I alas missed it for being “stuck” in a tea-house ceremony that lasted much longer than expected. (More later on that side issue!)

After the second poster session (with a highly original proposal by Radford Neal towards creating non-reversibility at the level of the uniform generator rather than later on), I thus only attended Emily Fox’s *Stochastic Gradient MCMC for Sequential Data Sources, *which superbly reviewed (in connection with a sequence of papers, including a recent one by Aicher et al.) error rate and convergence properties of stochastic gradient estimator methods there. Another paper I need to soon read!

The one before last speaker, Roman Novak, exposed a Python library about infinite neural networks, for which I had no direct connection (and talks I have always difficulties about libraries, even without a four hour sleep night) and the symposium concluded with a mild round-table. Mild because Frank Wood’s best efforts (and healthy skepticism about round tables!) to initiate controversies, we could not see much to bite from each other’s viewpoint.

**I** came by chance upon this 2018 entry in Synced that NeurIPS now takes place in Canada between Montréal and Vancouver primarily because visas to Canada are easier to get than visas to the USA, even though some researchers still get difficulties in securing theirs. Especially researchers from some African countries, which is exposed in the article as one of the reasons the next ICLR takes place in Addis Ababa. Which I wish I could attend! In the meanwhile, I will be taking part in an ABC workshop in Vancouver, December 08, prior to NeurIPS 2019, before visiting the Department of Statistics at UBC the day after. (My previous visit there was in 1990, I believe!) Incidentally but interestingly, the lottery entries for NeurIPS 2019 are **open till September 25**, to the public (those not contributing to the conference or any of its affiliated groups). This is certainly better than having bots buying all entries within 12 minutes of the opening time!

More globally, this entry makes me wonder how learned societies could invest in ensuring locations for their (international) meetings allow for a maximum inclusion in terms of these visa difficulties, but also ensuring freedom and safety for all members. Which may prove a *de facto* impossibility. For instance, Ethiopia has a rather poor record in terms of human rights and, in particular, homosexuality is criminalised there. An alternative would be to hold the conferences in parallel locations chosen to multiply the chances for this inclusion, but this could prove counter-productive [for inclusion] by creating groups that would never ever meet. An insolvable conundrum?

**A** long-time friend at UBC pointed out to me the opening of two tenure-track Assistant Professor positions at the Department of Statistics at the University of British Columbia, Vancouver, with an anticipated start date of July 1, 2020 or January 1, 2021. The deadline for applications is October 18, 2019. Statistics at UBC is an internationally renowned department, in particular (but not restricted to) computational statistics and Bayesian methods and this is a great opportunity to join this department. (Not mentioning the unique location of the campus and the beautiful surroundings of the city of Vancouver!)