Archive for Arianna Rosenbluth

j-ISBA Blackwell-Rosenbluth Award²⁶ [call reposted]

Posted in Statistics with tags , , , , , , , , , , , on May 15, 2026 by xi'an

Call for award nominations

Dear all,

It is with great pleasure that we announce the Blackwell-Rosenbluth Award by j-ISBA, a recently established award for junior researchers in different areas of Bayesian statistics. The award aims at recognizing outstanding junior Bayesian researchers based on their overall contribution to the field and to the community. There will be six winners in total who will be invited to present their work in two special events of the Junior Bayes Beyond the Borders (JB^3) webinar series and receive three years of free ISBA and j-ISBA membership.

ISBA proudly has a wide geographical diversity among its members. To encourage scientific exchange and strengthen research connections between geographies, three prizes will be awarded to researchers based in time zones UTC+0 to UTC+13 [e.g. Africa + Asia + Europe + Oceania] and three to those based in UTC-12 to UTC-1 [e.g. North America + South America]. We welcome nominations of junior researchers working in the broad spectrum of topics in Bayesian statistics, including but not limited to methods, theory, computation, machine learning, data science, biostatistics, econometrics, industrial statistics, environmental science, and software.

There will be two scientific committees: one representing UTC- and the other representing UTC+, each consisting of members from their respective regions based on their professional affiliations. These committees are tasked with evaluating candidates for the award. The UTC- committee will evaluate submissions from UTC+ and vice versa.

Why Blackwell-Rosenbluth

The award is named after David H. Blackwell and Arianna W. Rosenbluth for their groundbreaking works that lie at the foundation of modern Bayesian statistical theory and computation. They represent important role models for new researchers in Bayesian statistics.

David Harold Blackwell

Young Blackwell Born on April 24, 1919, Blackwell excelled in mathematics from an early age. He earned his doctoral degree from the University of Illinois at Urbana-Champaign under the supervision of Joseph L. Doob in 1941. He had a distinguished career, becoming a founding member in 1955 of the newly established Department of Statistics at University California, Berkeley. In 1965, he became the first African American to be elected member of the U.S. National Academy of Sciences and was awarded the John von Neumann Theory Prize in 1979. In addition to his seminal contributions to Bayesian inference, decision theory, game theory, sequential analysis and renewal theory, he also wrote one of the first textbooks in Bayesian statistics (Basic Statistics, McGraw-Hill, 1969).

Arianna Wright Rosenbluth

Young Rosenbluth Born on September 15, 1927, Arianna Wright Rosenbluth showed an affinity for sciences from early childhood. She completed her doctoral work under the supervision of a future Nobel Laureate, John Van Vleck, in 1949, making her the fifth woman to earn a Ph.D. in Physics from Harvard. Later, as a coauthor of the seminal 1953 paper introducing the Metropolis algorithm, Rosenbluth almost single-handedly implemented the algorithm on the MANIAC I hardware at the Los Alamos Scientific Laboratory. This made her the first person to ever implement the Markov chain Monte Carlo method when sophisticated programming tools were still years away, and the program had to be written in strings of 1’s and 0’s.

Eligibility and Application Procedure

Ph.D. students or early career researchers who obtained their PhD after January 1, 2021 are eligible for nomination. Candidates who were nominated in previous years may be nominated again if they received their Ph.D. after January 1, 2021. In exceptional cases, applicants who are more than five years past their Ph.D. may still be considered if they experienced a significant career break within five years of earning their degree (such as breaks due to illness, caring for a sick family member, pregnancy-related leave, or parental leave). Candidates may inquire about their eligibility, particularly if they have taken career breaks, by sending an email to jisba.section@gmail.com. A nomination may come from any ISBA member, including the nominee themselves. A nomination is to be submitted electronically and should contain:

  • Nominating letter in support of the candidate
  • CV of the candidate
  • One manuscript or alternate form of exposition (e.g. software documentation) of scientific work most representative of the nominee’s achievements; the submitted work should also be available as publication or in a public repository such as arXiv, bioRxiv, CRAN, Bioconductor or GitHub.

Timeline

Nominations for the 2026 award edition are now open! Deadline to submit is July 12, 2026

BayesComp 2025.2

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on June 19, 2025 by xi'an


The main BayesComp²⁵ conference started with Pierre Jacob’s plenary talk on his recent advances on coupling for unbiased MCMC—currently ERC grantee on that topic—.  Raising lazy questions like using a different target or transition kernel for the second chain in the coupling, connecting the Poisson equation and control variates, handling the signed issue with the unbiased approximations. Interestingly, they obtain an unbiased estimator of the asymptotic variance of the unbiased estimator. And a correction for self-normalised importance sampling, which has some connections with our 1996 (?) pinball sampler. Also an evaluation of the median of means, rather than the average of means, which is a thing I had been (lazily) contemplating for a while  (On the greedy side, as I was writing my recovery exam for my Monte Carlo course, I realised the results Pierre presented could be somewhat recycled into exam problems!)

My first parallel session was on gradient-based methods with a talk by Francesca Crucinio on proximal particle Langevin algorithms (similar to the one she gave in PariSanté last year), a talk by Zhihao Wang on stereographic multiple try Metropolis(-Rosenbluth-Teller) that unsurprisingly recovers ergodicity thanks to the compactness of the ball. For which I wonder why a Normal proposal makes complete sense since one could consider a mover after the projection instead and why iid rather than repelling multiple proposals are used… The last speaker was just out from the plane from California, Siddharth Vishwanath who spoke about repelling-attracting HMC. With very nice animations of HMC, if reaching the main point of using both negative and positive frictions a few minutes before the session finished. The method preserves volume and potential, if not energy.

Speaking of which (energy), I find myself struggling with my less than 6 hours of sleep since arrival during the first afternoon session, despite a fiery hot spot lunch, which means in plainer terms that I alas dozed in and out of the talks. The second session saw Jack Jewson exposing in deeper details the exact PDMP algorithm for Gibbs measures  Jeremias Knoblauch mentioned yesterday. And Jonathan Huggins as well, using Gaussian processes as proxies for expected likelihoods, with lower guarantees than pseudo-marginal versions. In a mildly connected way, Robin Ryder went through the resolution of the ecological inference challenge they produce with Nicolas Chopin and Théo Valdoire (all authors with whom I am connected, Théo being a brillant student of our MASH Master last year and now in Harvard, hopefully till the end of his PhD!)

On the extra-academic curriculum, I had a yummy dinner in the Maxwell Hawker (street) food centre, incl. Xiao Long Bao that cooled down fast enough to avoid the usual scalding effect, plus rojak a mixed fruit and vegetable fried in a peanut sauce that I had never tasted before, popiah (ditto), chili noodles, and an appam with durian deepfried balls as a fabulous and unexpected dessert.

JSM 2024, Portland, Day 3

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on August 9, 2024 by xi'an

Bayesian contributed session as the first round of the third day (with a choice of five parallel sessions featuring Bayesian topics!!, actually easier to pick than among the following eight parallel sessions of the 10:30 schedule!!!), with a talk by Tahir Ekin on adversarial outlier detection that could connect with our Oceaner(c) privacy concerns. Then one involving spike & slab (a theme to figure prominently in this special day!!) in mixed response models by Sameer Deshpande, seeking a (unBayesian!) MAP for a latent variable model by Monte Carlo EM. Followed by a talk by Yunyi Shen on completely random measures for estimating the (distribution of the) number of species in heterogeneous populations. Next, Valentin Zulj on (frequentist rather than) Bayesian stacking, on estimating optimal weights for model averaging (which should be posterior probabilities in a pure Bayesian mindframe), including a score function that could lead to generalised Bayesian inference on said weights. Finishing with a talk by Chaegeun Song on correcting Bayesian credible sets towards (frequentist, again!!!) exact coverage for classification (which reminded me of my very first paper with George on correcting frequentist confidence for Binomial observations). With which I could not really engage as seeking a specific coverage level did not seem relevant, imho, but I appreciated the wheel plot representation.My second morn session was about modern (what else?!) sampling algorithms, although I spent the first dozen minutes wondering whether or not I had entered the wrong room. Until Tianhao Wang focussed on Thompson sampling for bandits. It did prove far enough from my interest for my (sleep deprived) attention to drift too quickly. Only the talk by Yuchen Wu on a spike & slab (as suits the day!) challenge captured enough this wandering attention. Crossing further into my realm of primary topics by considering a target distribution that is a product of distributions. But I did not get from her presentation how a product measure decomposition was inducing higher efficiency (and did not find answers within the arXived preprint). Unless it exploited specific features of the target, like conditional independence between the components. The last talk was by Brice Huang on sampling low temperature Gibbs measures using stochastic localisation.

After coming upon a row of food trucks across the conference centre and being unfairly attracted by an Ethiopian injera picture into a terrible wrap, I returned for the Skeptical about AI session, just a few minutes late, only to find accessing the session was impossible! Quite sad to miss the presentations and the arguments (even though I had heard a previous talk by Genevera Allen when visiting Rutgers two years ago). As a second best, I then joined the recent (of course!) Advances in Bayesian Computation (aka ABC?!) session with a medley of topics, including a data subset versus data sketching model reduction by Sudipto Saha. Which could have consequences on our privacy strategies. And marginal evidence estimation for the Bayesian Lasso by Christopher Hans while avoiding data completion. And another latent variable model with a sequential variational Bayes approach by Bao Anh Vu, using at one point Cappé et al. (2005) EM-based approximation to the log likelihood gradient. Finishing by a back-to-the-future talk by Luke Duttweiler on MCMC convergence diagnostics. Comparing several chains via proximity maps that themselves require some preliminary knowledge about the MCMC kernel. (Nice title though, “the traceplot thickens”!)The crux of the day was however the 2024 COPSS Award ceremony with several friends featuring among the recipients, Danielle Durante for the Emerging Leaders Award, Regina Liu for the Elizabeth L. Scott Award and Veronika Rockova for the Presidents’ Award. Congrats!!!



combining normalizing flows and QMC

Posted in Books, Kids, Statistics with tags , , , , , , , , , , , , , on January 23, 2024 by xi'an

My PhD student Charly Andral [presented at the mostly Monte Carlo seminar and] arXived a new preprint yesterday, on training a normalizing flow network as an importance sampler (as in Gabrié et al.) or an independent Metropolis proposal, and exploiting its invertibility to call quasi-Monte Carlo low discrepancy sequences to boost its efficiency. (Training the flow is not covered by the paper.) This extends the recent study of He et al. (which was presented at MCM 2023 in Paris) to the normalising flow setting. In the current experiments, the randomized QMC samples are computed using the SciPy package (Roy et al. 2023), where the Sobol’ sequence is based on Joe and Kuo (2008) and on Matouˇsek (1998) for the scrambling, and where the Halton sequence is based on Owen (2017). (No pure QMC was harmed in the process!) The flows are constructed using the package FlowMC. As expected the QMC version brings a significant improvement in the quality of the Monte Carlo approximations, for equivalent computing times, with however a rapid decrease in the efficiency as the dimension of the targetted distribution increases. On the other hand, the architecture of the flow demonstrates little relevance. And the type of  RQMC sequence makes a difference, the advantage apparently going to a scrambled Sobol’ sequence.

Quantum-enhanced Markov chain Monte Carlo

Posted in Books, Statistics with tags , , , , , on September 2, 2023 by xi'an

A rare occurrence of an MCMC paper in Nature!!! David Layden and co-authors published this paper on 12 July, about using a quantum proposal in a Metropolis-Rosenbluth-Hastings simulation of an Ising model. More specifically, based on “quenched dynamics of a transverse-field quantum Ising model20, which can be efficiently simulated on a quantum computer21“, which amounts to using a Hamiltonian proposal. I tried to dig through the supplementary material to understand the implementation and the requirement for a quantum computer, but failed… (The picture below is from the News & Views tribune on the paper. It does not help.) The above illustrates the ability of the algorithm to explore more efficiently likely low energy configurations of the Ising model when compared with standard solutions, although I could not fathom if the time cost of resorting to the quantum computer for the former was accounted for.

“In experiments, our quantum algorithm converged in fewer iterations than common classical MCMC alternatives, suggesting unusual robustness to noise”

While this paper is a significant foray into quantum MCMC, its target is the modest Ising model for n=10 nodes, with very special features that seem to contribute to the construction of the proposal. A model that can be exactly simulated, either directly for that size or by perfect sampling à la Propp & Wilson for larger n’s. And whose discretisation is not too far from the model considered by Metropolis, [mostly] the Rosenbluths, and the Tellers in the 1950’s. It thus remains to see how extensions can be built for more realistic targets.