Just received the good news that our paper Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale by Wu Changye (吴昌烨), Pierre Pudlo, Julien Stoehr and myself, got accepted in Statistics & Computing! This is great in its own, but further concludes a story that started with Changye’s PhD thesis at Paris Dauphine in 2018, with a revision request from Statistics & Computing that stalled with Changye’s departing for industry in Shanghai and eventually resumed thanks to Julien’s massive investment in coding and improving the learning mechanism. It may also conclude my story with Statistics & Computing, where I am supposed to be the historically most prolific author (?), given the move by Springer to a cash-flow model on 01 January, 2027…
Archive for revision
faster HMC by learning
Posted in Books, Kids, Statistics, University life with tags algorithm, conformal Hamiltonian dynamics, eHMC, Hamiltonian Monte Carlo, HMC, leapfrog integrator, mirror descent, No-U-Turn sampler, NUTS, PhD thesis, population Monte Carlo, publication fees, revision, Scholarly Open Access, Shanghai, Springer Nature, Statistics & Computing, tempering, Università Ca' Foscari Venezia on September 23, 2026 by xi'anBayesian Adversarial Privacy [v2]
Posted in Books, Statistics, University life with tags #ERCSyG, Akaike Lecture, Alice and Bob, arXiv, Bayesian decision theory, contextual integrity, differential privacy, Durham, game theory, ICML 2026, Japan, lecture, Ocean, persuasive privacy, privacy, probabilistic differential privacy, R, R-U map, revision, risk ratio, slides, synthetic data, the Japanese Societies of Statistical Science Conference, U) map, Université Paris Dauphine on September 11, 2026 by xi'an
We have just reposted our paper Bayesian Adversarial Privacy on arXiv to reflect the revision we wrote in the past months, to address the (quite sensible) comments from the reviewers. Interestingly the discussants of my Akaike lecture made similar points. The main changes are in explaining more clearly the nature of the combined loss, with Antoine coming up with the use of illuminating R-U map representations, in enlarging the references to other approaches, in stressing that Eve was an Alice’s construct rather than a genuine adversary, but still integrating the case of “multiple Eves”, in mellowing our criticisms of DP, and in expanding the conclusion with limitations and extensions subsections.
insufficient Gibbs sampling bridges as well!
Posted in Books, Kids, pictures, R, Statistics, University life with tags ABC, ABC model choice, Bayes factor, bridge sampling, completion, evidence, Gibbs sampling, insufficient statistic, latent variable, mad, median, model comparison, revision on March 3, 2024 by xi'an
Antoine Luciano, Robin Ryder and I posted a revised version of our insufficient Gibbs sampler on arXiv last week (along with three other revisions or new deposits of mine’s!), following comments and suggestions from referees. Thanks to this revision, we realised that the evidence based on an (insufficient) statistic was also available for approximation by a Monte Carlo estimate attached to the completed sample simulated by the insufficient sampler. Better, a bridge sampling estimator can be used in the same conditions as when the full data is available! In this new version, we thus revisited toy examples first explored in some of my ABC papers on testing (with insufficient statistics), as illustrated by both graphs on this post.
computing Bayes 2.0
Posted in Books, Statistics, University life with tags Approximate Bayesian computation, arXiv, ASSA, Australia, Bayesian computing, MCMC, Monash University, Monte Carlo methods, Monte Carlo Statistical Methods, review, revision, survey on December 11, 2020 by xi'an
Our survey paper on “computing Bayes“, written with my friends Gael Martin [who led this project most efficiently!] and David Frazier, has now been revised and resubmitted, the new version being now available on arXiv. Recognising that the entire range of the literature cannot be encompassed within a single review, esp. wrt the theoretical advances made on MCMC, the revised version is more focussed on the approximative solutions (when considering MCMC as “exact”!). As put by one of the referees [which were all very supportive of the paper], “the authors are very brave. To cover in a review paper the computational methods for Bayesian inference is indeed a monumental task and in a way an hopeless one”. This is the opportunity to congratulate Gael on her election to the Academy of Social Sciences of Australia last month. (Along with her colleague from Monash, Rob Hyndman.)
Nested Sampling SMC [a reply]
Posted in Books, Statistics, University life with tags ANS-SMC, Australia, nested sampling, phase transition, Queensland University of Technology, reply, revision, short term memory, SMC, temperature schedule on April 9, 2020 by xi'anHere is a response from Robert Salomone following my comments of the earlier day (and pointing out I already commented the paper two years ago):
You may be interested to know that we are at the tail end of carrying out a major revision of the paper, which we hope will be done in the near future — there will be some new theory (we are in the final stages for a consistency proof of the ANS-SMC algorithm with new co-author Adam Johansen), as well as new numerics (including comparisons to Nested Sampling), and additional discussion that clarifies the overall narrative.A few comments relating your post that may clear some things up:
- The method you describe with the auxiliary variable is actually one of three proposed algorithms. We call this one “Improved Nested Sampling” as it is the algorithm most similar to the original Nested Sampling. Two further extensions are the adaptive SMC sampler, and the fixed SMC sampler – the latter of which is provably consistent and unbiased for the model evidence (we also often see improvements over standard NS for similar computational effort when MCMC is used).
- Regarding computational effort – it is the same for Improved NS (in fact, you can obtain the standard Nested Sampling evidence estimate from the same computational run!). For the adaptive variant, the computational effort is roughly the same for ρ = e⁻¹. In the current version of the paper this is only discussed briefly (last page of p.23). However, in the revision we will include additional experiments comparing the practical performance.
- Regarding the question of “why not regular SMC”; we chose to focus more on why SMC is a good way to do Nested Sampling rather than why Nested Sampling is a good way to do SMC. Our main priority was to show there is a lot of opportunity to develop new nested sampling style algorithms by approaching it from a different angle. That said, Nested Sampling’s primary advantage over standard SMC seems to be in problems involving “phase transitions’’ such as our first example, for which temperature based methods are inherently ill-suited (and will often fail to detect so!).
