off to SimStat2019, Salzburg

Today, I am off to Salzburg for the SimStat 2019 workshop, or more formally the 10th International Workshop on Simulation and Statistics, where I give a talk on ABC. The program of the workshop is quite diverse and rich and so I do not think I will have time to take advantage of the Hohe Tauern or the Berchtesgaden Alps to go climbing. Especially since I am also discussing papers in an ABC session.

Stein’s method in machine learning [workshop]

There will be an ICML workshop on Stein’s method in machine learning & statistics, next July 14 or 15, located in Long Beach, CA. Organised by François-Xavier Briol (formerly Warwick), Lester Mckey, Chris Oates (formerly Warwick), Qiang Liu, and Larry Golstein. To quote from the webpage of the workshop

Stein’s method is a technique from probability theory for bounding the distance between probability measures using differential and difference operators. Although the method was initially designed as a technique for proving central limit theorems, it has recently caught the attention of the machine learning (ML) community and has been used for a variety of practical tasks. Recent applications include goodness-of-fit testing, generative modeling, global non-convex optimisation, variational inference, de novo sampling, constructing powerful control variates for Monte Carlo variance reduction, and measuring the quality of Markov chain Monte Carlo algorithms.

Speakers include Anima Anandkumar, Lawrence Carin, Louis Chen, Andrew Duncan, Arthur Gretton, and Susan Holmes. I am quite sorry to miss two workshops dedicated to Stein’s work in a row, the other one being at NUS, Singapore, around the Stein paradox.

the future of conferences

The last issue of Nature for 2018 offers a stunning collection of science photographs, ten portraits of people who mattered (for the editorial board of Nature), and a collection of journalists’ entries on scientific conferences. The later point leading to interesting questioning on the future of conferences, some of which relate to earlier entries on this blog. Like attempts to make them having a lesser carbon footprint, by only attending focused conferences and workshops, warning about predatory ones, creating local hives on different continents that can partake of all talks but reduce travel and size and still allow for exchanges person to person, multiply the meetings and opportunities around a major conference to induce “only” one major trip (as in the past summer of British conferences, or the incoming geographical combination of BNP and O’Bayes 2019), cut the traditional dreary succession of short talks in parallel in favour of “unconferences” where participants set communally the themes and  structure of the meeting (but ware the dangers of bias brought by language, culture, seniority!). Of course, this move towards new formats will meet opposition from several corners, including administrators who too often see conferences as a pretense for paid vacations and refuse supporting costs without a “concrete” proof of work in the form of a presentation.Another aspect of conference was discussed there, namely the art of delivering great talks. Which is indeed more an art than a science, since the impact will not only depend on the speaker and the slides, but also on the audience and the circumstances. As years pile on, I am getting less stressed and probably too relaxed about giving talks, but still rarely feel I have reached toward enough of the audience. And still falling too easily for the infodump mistake… Which reminds me of a recent column in Significance (although I cannot link to it!), complaining about “finding it hard or impossible to follow many presentations, particularly those that involved a large number of equations.” Which sounds strange to me as on the opposite I quickly loose track in talks with no equations. And as mathematical statistics or probability issues seems to imply the use of maths symbols and equations. (This reminded me of a short course I gave once in a undisclosed location, where a portion of the audience left after the first morning, due to my use of “too many Greek letters”.) Actually, I am always annoyed at apologies for using proper maths notations, since they are the tools of our trade.Another entry of importance in this issue of Nature is an interview with Katherine Heller and Hal Daumé, as first chairs for diversity and inclusion at N[eur]IPS. Where they discuss the actions taken since the previous NIPS 2017 meeting to address the lack of inclusiveness and the harassment cases exposed there, first by Kristian Lum, Lead Statistician at the Human Rights Data Analysis Group (HRDAG), whose blog denunciation set the wheels turning towards a safer and better environment (in stats as well as machine-learning). This included the [last minute] move towards renaming the conference as NeuroIPS to avoid sexual puns on the former acronym (which as a non-native speaker I missed until it was pointed out to me!). Judging from the feedback it seems that the wheels have indeed turned a significant amount and hopefully will continue its progress.

off to Luminy for a second Bayesian week

computational statistics and molecular simulation [18w5023]

The last day of the X fertilisation workshop at the casa matematicà Oaxaca, there were only three talks and only half of the participants. I lost the subtleties of the first talk by Andrea Agazzi on large deviations for chemical reactions, due to an emergency at work (Warwick). The second talk by Igor Barahona was somewhat disconnected from the rest of the conference, working on document textual analysis by way of algebraic data analysis (analyse des données) methods à la Benzécri. (Who was my office neighbour at Jussieu in the early 1990s.) In the last and final talk, Eric Vanden-Eijden made a link between importance sampling and PDMP, as an integral can be expressed via a trajectory of a path. A generalisation of path sampling, for almost any ODE. But also a competitor to nested sampling, waiting for the path to reach an Hamiltonian level, without some of the difficulties plaguing nested sampling like resampling. And involving continuous time processes. (Is there a continuous time version of ABC as well?!) Returning unbiased estimators of mean (the original integral) and variance. Example of a mixture example in dimension d=10 with k=50 components using only 100 paths.

computational statistics and molecular simulation [18w5023]

This Thursday, our X fertilisation workshop at the interface between molecular dynamics and Monte Carlo statistical methods saw a wee bit of reduction in the audience as some participants had already left Oaxaca. Meaning they missed the talk of Christophe Andrieu on hypocoercivity which could have been another hand-on lecture, given the highly pedagogical contents of the talk. I had seen some parts of the talk in MCqMC 2018 in Rennes and at NUS, but still enjoyed the whole of it very much, and so did the audience given the induced discussion. For instance, previously, I had not seen the connection between the guided random walks of Gustafson and Diaconis, and continuous time processes like PDMP. Which Christophe also covered in his talk. (Also making me realise my colleague Jean Dolbeault in Dauphine was strongly involved in the theoretical analysis of PDMPs!) Then Samuel Power gave another perspective on PDMPs. With another augmentation, connected with time, what he calls trajectorial reversibility. This has the impact of diminishing the event rate, but creates some kind of reversibility which seems to go against the motivation for PDMPs. (Remember that all talks are available as videos on the BIRS webpage.) A remark in the talk worth reiterating is the importance of figuring out which kinds of approximations are acceptable in these approximations. Connecting somewhat with the next talk by Luc Rey-Bellet on a theory of robust approximations. In the sense of Poincaré, Gibbs, Bernstein, &tc. concentration inequalities and large deviations. With applications to rare events.The fourth and final “hand-on” session was run by Miranda Holmes-Certon on simulating under constraints. Motivated by research on colloids. For which the overdamp Langevin diffusion applies as an accurate model, surprisingly. Which makes a major change from the other talks [most of the workshop!] relying on this diffusion. (With an interesting intermede on molecular velcro made of DNA strands.) Connected with this example, exotic energy landscapes are better described by hard constraints. (Potentially interesting extension to the case when there are too many constraints to explore all of them?) Now, the definition of the measure projected on the manifold defined by the constraints is obviously an important step in simulating the distribution, which density is induced by the gradient of the constraints ∇q(x). The proposed algorithm is in the same spirit as the one presented by Tony the previous day, namely moving along the tangent space then on the normal space to get back to the manifold. A solution that causes issues when the gradient is (near) zero. A great hand-on session which induced massive feedback from the audience.

In the afternoon session, Gersende Fort gave a talk on a generalisation of the Wang-Landau algorithm, which modifies the true weights of the elements of a partition of the sampling space, to increase visits to low [probability] elements and jumps between modes. The idea is to rely on tempered versions of the original weights, learned by stochastic approximation. With an extra layer of adaptivity. Leading to an improvement with parameters that depends on the phase of the stochastic approximation. The second talk was by David Sanders on a recent paper in Chaos about importance sampling for rare events of (deterministic) billiard dynamics. With diffusive limits which tails are hard to evaluate, except by importance sampling. And the last talk of the day was by Anton Martinsson on simulated tempering for a molecular alignment problem. With weights of different temperatures proportional to the inverse of the corresponding normalising constants, which themselves can be learned by a form of bridge sampling  if I got it right.

On a very minor note, I heard at breakfast a pretty good story from a fellow participant having to give a talk at a conference that was moved to a very early time in the morning due to an official appearing at a later time and as a result “enjoying” a very small audience to the point that a cleaning lady appeared and started cleaning the board as she could not conceive the talks had already started! Reminding me of this picture at IHP.

computational statistics and molecular simulation [18w5023]

 I truly missed the gist of the first talk of the Wednesday morning of our X fertilisation workshop by Jianfeng Lu partly due to notations, although the topic very much correlated to my interests like path sampling, with an augmented version of HMC using an auxiliary indicator. And mentions made of BAOAB. Next, Marcello Pereyra spoke about Bayesian image analysis, with the difficulty of setting a prior on an image. In case of astronomical images there are motivations for an L¹ penalisation sparse prior. Sampling is an issue. Moreau-Yoshida proximal optimisation is used instead, in connection with our MCMC survey published in Stats & Computing two years ago. Transferability was a new concept for me, as introduced by Kerrie Mengersen (QUT), to extrapolate an estimated model to another system without using the posterior as a prior. With a great interlude about the crown of thorns starfish killer robot! Rather a prior determination based on historical data, in connection with recent (2018) Technometrics and Bayesian Analysis papers towards rejecting non-plausible priors. Without reading the papers (!), and before discussing the matter with Kerrie, here or in Marseille, I wonder at which level of precision this can be conducted. The use of summary statistics for prior calibration gave the approach an ABC flavour.

The hand-on session was Jonathan Mattingly’s discussion of gerrymandering reflecting on his experience at court! Hard to beat for an engaging talk reaching between communities. As it happens I discussed the original paper last year. Of course it was much more exciting to listen to Jonathan explaining his vision of the problem! Too bad I “had” to leave before the end for a [most enjoyable] rock climbing afternoon… To be continued at the dinner table! (Plus we got the complete explanation of the term gerrymandering, including this salamander rendering of the first identified as gerrymandered district!)