Archive for optimal transport

ISBA 2021.3

Posted in Kids, Mountains, pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on July 1, 2021 by xi'an

Now on the third day which again started early with a 100% local j-ISBA session. (After a group run to and around Mont Puget, my first real run since 2020!!!) With a second round of talks by junior researchers from master to postdoc level. Again well-attended. A talk about Bayesian non-parametric sequential taxinomy by Alessandro Zito used the BayesANT acronym, which reminded me of the new vave group Adam and the Ants I was listening to forty years ago, in case they need a song as well as a logo! (Note that BayesANT is also used for a robot using Bayesian optimisation!) And more generally a wide variety in the themes. Thanks to the j-organisers of this 100% live session!

The next session was on PDMPs, which I helped organise, with Manon Michel speaking from Marseille, exploiting the symmetry around the gradient, which is distribution-free! Then, remotely, Kengo Kamatani, speaking from Tokyo, who expanded the high-dimensional scaling limit to the Zig-Zag sampler, exhibiting an argument against small refreshment rates, and Murray Pollock, from Newcastle, who exposed quite clearly the working principles of the Restore algorithm, including why coupling from the past was available in this setting. A well-attended session despite the early hour (in the USA).

Another session of interest for me [which I attended by myself as everyone else was at lunch in CIRM!] was the contributed C16 on variational and scalable inference that included a talk on hierarchical Monte Carlo fusion (with my friends Gareth and Murray as co-authors), Darren’s call to adopt functional programming in order to save Bayesian computing from extinction, normalising flows for modularisation, and Dennis’ adversarial solutions for Bayesian design, avoiding the computation of the evidence.

Wes Johnson’s lecture was about stories with setting prior distributions based on experts’ opinions. Which reminded me of the short paper Kaniav Kamary and myself wrote about ten years ago, in response to a paper on the topic in the American Statistician. And could not understand the discrepancy between two Bayes factors based on Normal versus Cauchy priors, until I was told they were mistakenly used repeatedly.

Rushing out of dinner, I attended both the non-parametric session (live with Marta and Antonio!) and the high-dimension computational session on Bayesian model choice (mute!). A bit of a schizophrenic moment, but allowing to get a rough picture in both areas. At once. Including an adaptive MCMC scheme for selecting models by Jim Griffin. Which could be run directly over the model space. With my ever-going wondering at the meaning of neighbour models.

ISBA 20.2.21

Posted in Kids, Mountains, pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , on June 30, 2021 by xi'an

A second day which started earlier and more smoothly with a 100% local j-ISBA session. (Not counting the invigorating swim in Morgiou!) With talks by junior researchers from master to postdoc level, as this ISBA mirror meeting was primarily designed for them, so that they could all present their work, towards gaining more visibility for their research and facilitating more interactions with the participants. CIRM also insisted on this aspect of the workshop, which was well-attended.

I alas had to skip the poster session [and the joys of gather.town] despite skipping lunch [BND], due to organisational constraints. Then attended the Approximate Bayesian computation section, including one talk by Geoff Nicholls on confidence estimation for ABC, following upon the talk given by Kate last evening. And one by Florian Maire on learning the bound in accept-reject algorithms on the go, as in Caffo et al. (2002), which I found quite exciting and opening new possibilities, esp. if the Markov chain thus produced can be recycled towards unbiasedness without getting the constant right! For instance, by Rao-Blackwellisation, multiple mixtures, black box importance sampling, whatever. (This also reminded me of the earlier Goffinet et al. 1996.)

Followed by another Bayesian (modeling and) computation session. With my old friend Peter Müller talking about mixture inference with dependent priors (and a saturated colour scheme!), Matteo Ruggieri [who could not make it to CIRM!] on computable Bayesian inference for HMMs. Providing an impressive improvement upon particle filters for approximating the evidence. Also bringing the most realistic Chinese restaurant with conveyor belt! And Ming Yuan Zhou using optimal transport to define distance between distributions. With two different conditional distributions depending on which marginal is first fixed. And a connection with GANs (of course!).

And it was great to watch and listen to my friend Alicia Carriquiry talking on forensic statistics and the case for (or not?!) Bayes factors. And remembering Dennis Lindley. And my friend Jim Berger on frequentism versus Bayes! Consistency seems innocuous as most Bayes procedures are. Empirical coverage is another kind of consistency, isn’t it?

A remark I made when re-typing the program for CIRM is that there are surprisingly few talks about COVID-19 overall, maybe due to the program being mostly set for ISBA 2020 in Kunming. Maybe because we are more cautious than the competition…?!

And, at last, despite a higher density of boars around the CIRM facilities, no one got hurt yesterday! Unless one counts the impact of the French defeat at the Euro 2021 on the football fans here…

transport Monte Carlo

Posted in Books, pictures, Statistics, Travel with tags , , , , , , , , , , , , , , , on August 31, 2020 by xi'an

Read this recent arXival by Leo Duan (from UF in Gainesville) on transport approaches to approximate Bayesian computation, in connection with normalising flows. The author points out a “lack of flexibility in a large class of normalizing flows”  to bring forward his own proposal.

“…we assume the reference (a multivariate uniform distribution) can be written as a mixture of many one-to-one transforms from the posterior”

The transportation problem is turned into defining a joint distribution on (β,θ) such that θ is marginally distributed from the posterior and β is one of an infinite collection of transforms of θ. Which sounds quite different from normalizing flows, to be sure. Reverting the order, if one manages to simulate β from its marginal the resulting θ is one of the transforms. Chosen to be a location-scale modification of β, s⊗β+m. The weights when going from θ to β are logistic transforms with Dirichlet distributed scales. All with parameters to be optimised by minimising the Kullback-Leibler distance between the reference measure on β and its inverse mixture approximation, and resorting to gradient descent. (This may sound a wee bit overwhelming as an approximation strategy and I actually had to make a large cup of strong macha to get over it, but this may be due to the heat wave occurring at the same time!) Drawing θ from this approximation is custom-made straightforward and an MCMC correction can even be added, resulting in an independent Metropolis-Hastings version since the acceptance ratio remains computable. Although this may defeat the whole purpose of the exercise by stalling the chain if the approximation is poor (hence suggesting this last step being used instead as a control.)

The paper also contains a theoretical section that studies the approximation error, going to zero as the number of terms in the mixture, K, goes to infinity. Including a Monte Carlo error in log(n)/n (and incidentally quoting a result from my former HoD at Paris 6, Paul Deheuvels). Numerical experiments show domination or equivalence with some other solutions, e.g. being much faster than HMC, the remaining $1000 question being of course the on-line evaluation of the quality of the approximation.

distortion estimates for approximate Bayesian inference

Posted in pictures, Statistics, University life with tags , , , , , , , , , on July 7, 2020 by xi'an

A few days ago, Hanwen Xing, Geoff Nichols and Jeong Eun Lee arXived a paper with the following title, to be presented at uai2020. Towards assessing the fit of the approximation for the actual posterior, given the available data. This covers of course ABC methods (which seems to be the primary focus of the paper) but also variational inference and synthetic likelihood versions. For a parameter of interest, the difference between exact and approximate marginal posterior distributions is see as a distortion map, D = F o G⁻¹, interpreted as in optimal transport and estimated by normalising flows. Even when the approximate distribution G is poorly estimated since D remains the cdf of G(X) when X is distributed from F. The marginal posterior approximate cdf G can be estimated by ABC or another approximate technique. The distortion function D is itself restricted to be a Beta cdf, with parameters estimated by a neural network (although based on which input is unclear to me, unless the weights in (5) are the neural weights). The assessment is based on the estimated distortion at the dataset, as a significant difference from the identity signal a poor fit for the approximation. Overall, the procedure seems implementable rather easily and while depending on calibrating choices (other than the number of layers in the neural network) a realistic version of the simulation-based diagnostic of Talts et al. (2018).

Gabriel’s talk at Warwick on optimal transport

Posted in Statistics with tags , , , , , , on March 4, 2020 by xi'an