my demonic talk

Laplace’s Demon [coming home!]

A new online seminar is starting this week, called Laplace’s Demon [after too much immersion in His Dark Materials, lately, ather than Unix coding, I first wrote daemon!] and concerned with Bayesian Machine Learning at Scale. Run by Criteo in Paris (hence the Laplace filiation, I presume!). Here is the motivational blurb from their webpage

Machine learning is changing the world we live in at a break neck pace. From image recognition and generation, to the deployment of recommender systems, it seems to be breaking new ground constantly and influencing almost every aspect of our lives. In this seminar series we ask distinguished speakers to comment on what role Bayesian statistics and Bayesian machine learning have in this rapidly changing landscape. Do we need to optimally process information or borrow strength in the big data era? Are philosophical concepts such as coherence and the likelihood principle relevant when you are running a large scale recommender system? Are variational approximations, MCMC or EP appropriate in a production environment? Can I use the propensity score and call myself a Bayesian? How can I elicit a prior over a massive dataset? Is Bayes a reasonable theory of how to be perfect but a hopeless theory of how to be good? Do we need Bayes when we can just A/B test? What combinations of pragmatism and idealism can be used to deploy Bayesian machine learning in a large scale live system? We ask Bayesian believers, Bayesian pragmatists and Bayesian skeptics to comment on all of these subjects and more.

The seminar takes places on the second Wednesday of the month, at 5pm (GMT+2) starting ill-fatedly with myself on ABC-Gibbs this very Wednesday (13 May 2020), followed by Aki Vehtari, John Ormerod, Nicolas Chopin, François Caron, Pierre Latouche, Victor Elvira, Sara Filippi, and Chris Oates. (I think my very first webinar was a presentation at the Deutsche Bank, New York, I gave from CREST videoconference room from 8pm till midnight after my trip was cancelled when the Twin Towers got destroyed, on 07 September 2001…)

rare ABC [webinar impressions]

A second occurrence of the One World ABC seminar by Ivis Kerama, and Richard Everitt (Warwick U), on their on-going pape with and Tom Thorne, Rare Event ABC-SMC², which is not about rare event simulation but truly about ABC improvement. Building upon a previous paper by Prangle et al. (2018). And also connected with Dennis’ talk a fortnight ago in that it exploits an autoencoder representation of the simulated outcome being H(u,θ). It also reminded me of an earlier talk by Nicolas Chopin.

This approach avoids using summary statistics (but relies on a particular distance) and implements a biased sampling of the u’s to produce outcomes more suited to the observation(s). Almost sounds like a fiducial ABC! Their stopping rule for decreasing the tolerance is to spot an increase in the variance of the likelihood estimates. As the method requires many data generations for a single θ, it only applies in certain settings. The ABC approximation is indeed used as an estimation of likelihood ratio (which makes sense for SMC² but is biased because of ABC). I got slightly confused during Richard’s talk by his using the term of unbiased estimator of the likelihood before I realised he was talking of the ABC posterior. Thanks to both speakers, looking forward the talk by Umberto Picchini in a fortnight (on a joint paper with Richard).

value of a chess game

In our (internal) webinar at CEREMADE today, Miguel Oliu Barton gave a talk on the recent result his student Luc Attia and himself obtained, namely a tractable way of finding the value of a game (when minimax equals maximin), result that got recently published in PNAS:

“Stochastic games were introduced by the Nobel Memorial Prize winner Lloyd Shapley in 1953 to model dynamic interactions in which the environment changes in response to the players’ behavior. The theory of stochastic games and its applications have been studied in several scientific disciplines, including economics, operations research, evolutionary biology, and computer science. In addition, mathematical tools that were used and developed in the study of stochastic games are used by mathematicians and computer scientists in other fields. This paper contributes to the theory of stochastic games by providing a tractable formula for the value of finite competitive stochastic games. This result settles a major open problem which remained unsolved for nearly 40 years.”

While I did not see a direct consequence of this result in regular statistics, I found most interesting the comment made at one point that chess (with forced nullity after repetitions) had a value, by virtue of Zermelo’s theorem. As I had never considered the question (contrary to Shannon!). This value remains unknown.

ABC webinar, first!

The première of the ABC World Seminar last Thursday was most successful! It took place at the scheduled time, with no technical interruption and allowed 130⁺ participants from most of the World [sorry, West Coast friends!] to listen to the first speaker, Dennis Prangle,  presenting normalising flows and distilled importance sampling. And to answer questions. As I had already commented on the earlier version of his paper, I will not reproduce them here. In short, I remain uncertain, albeit not skeptical, about the notions of normalising flows and variational encoders for estimating densities, when perceived as a non-parametric estimator due to the large number of parameters it involves and wonder at the availability of convergence rates. Incidentally, I had forgotten at the remarkable link between KL distance & importance sampling variability. Adding to the to-read list Müller et al. (2018) on neural importance sampling.

ABC World seminar

With most of the World being more or less confined at home, conferences cancelled one after the other, including ABC in Grenoble!, we are launching a fortnightly webinar on approximation Bayesian computation, methods, and inference. The idea is to gather members and disseminate results and innovation during these coming weeks and months under lock-down. And hopefully after!

At this point, the interface will be Blackboard Collaborate, run from Edinburgh by Michael Gutmann, for which neither registration nor software is required. Before each talk, a guest link will be mailed to the mailing list. Please register here to join the list.

The seminar is planned on Thursdays at either 9am or more likely 11:30 am UK (+1GMT) time, as we are still debating the best schedule to reach as many populated time zones as possible!, and the first speakers are

09.04.2020	Dennis Prangle	Distilling importance sampling
23.04.2020	Ivis Kerama and Richard Everitt	Rare event SMC²
07.05.2020	Umberto Picchini	Stratified sampling and bootstrapping for ABC

Bayesian webinar: Bayesian conjugate gradient

Bayesian Analysis is launching its webinar series on discussion papers! Meaning the first 90 registrants will be able to participate interactively via the Zoom Conference platform while additional registrants will be able to view the Webinar on a dedicated YouTube Channel. This fantastic initiative is starting with the Bayesian conjugate gradient method of Jon Cockayne (University of Warwick) et al., on October 2 at 4pm Greenwich time. (With available equivalences for other time zones!) I strongly support this initiative and wish it the widest possible success, as it could bring a new standard for conferences, having distant participants gathering in a nearby location to present talks and attend other talks from another part of the World, while effectively participating. An dense enough network could even see the non-stop conference emerging!