Archive for manifold
many folks for manifolds [computational methods for probability distributions on manifolds workshop]
Posted in pictures, Travel, University life with tags bootstrap, experimental design, France, generative model, Gromov-Wasserstein, group picture, IHP, Institut Henri Poincaré, inverse problems, kernel Stein discrepancy descent, manifold, manifold exploration, MCMC, mixture models, Paris, workshop on May 27, 2026 by xi'an
computational methods for probability distributions on manifolds (11-13 May, IHP, Paris)
Posted in Books, pictures, Statistics, Travel, University life with tags bootstrap, experimental design, generative model, Gromov-Wasserstein, IHP, Institut Henri Poincaré, inverse problems, kernel Stein discrepancy descent, manifold, manifold exploration, MCMC, mixture models, Paris, workshop on May 12, 2026 by xi'an
This week, we are running a small workshop on Computational methods for probability distributions on manifolds, whose size was dictated by the corresponding surface of the Institut Henri room allotted to us by the IHP administration. Very exciting theme and very exciting program, which more than make up for the unseasonal weather in Paris.
May 11
Guillaume Pouliot – MCMC on Manifolds in Economics
Alessandro Barp – Kernel and Stein discrepancies between distributions, à la Schwartz
Robin Ryder – Coupling MCMC on manifolds
Chang-Han Rhee – Experimental Design on Manifolds
May 12
Gilles Vilmart – High-order sampling of the invariant distribution of ergodic stochastic dynamics: preconditioning and postprocessing
Paul Breiding – Sampling from or near nonlinear algebraic varieties
Nick Whiteley – Statistical exploration of the Manifold Hypothesis
Judith Rousseau – Denoising diffusion Models under the Manifold Hypothesis : A dimension free convergence rate
Manon Michel – Convergence of non-reversible Markov processes via lifting and Flow Poincaré inequality
Tobias Grafke – Sampling Conditioned Diffusions via Pathspace Projected Monte Carlo
Miranda Holmes-Cerfon – Simulating sticky Brownian motion
Agnès Desolneux – Distances “à la Gromov-Wasserstein” for Gaussian Mixture Models
May 13
Giovanni Samaey – Multilevel interacting particle methods for sampling Bayesian inverse problems
Marylou Gabrié – Revisiting enhanced sampling driven by collective variables using generative models
Chris Walker – A Bayesian Perspective on the Maximum Score Problem
Lulu Kang – Active Learning for Manifold Gaussian Process Regression
OWAB [season] I
Posted in Statistics, University life with tags ABC, Approximate Bayesian computation, approximate Bayesian inference, Bayesian inference, bluesky, Constrained Monte Carlo, COVID-19, Linkedin, lockdown, manifold, normalizing flow, One World ABC Seminar, One World Approximate Bayesian Inference Seminar, OWABI, Season's greetings, simulation-based inference, twitter, University of Warwick, webinar on October 28, 2024 by xi'an
OWABC is dead, long life OWABI! After 5 seasons of the One World Approximate Bayesian Computation (ABC) Seminar, launched in April 2020 (!) to gather members and disseminate results and innovation during those weeks and months under lockdown, the organisers (incl. yours truly) have now decided to launch a “new” seminar series, the One World Approximate Bayesian Inference (OWABI) Seminar, to better reflect the broader interest and scope of this series, which goes beyond ABC. With Bluesky, X, and Linkedin accountsWith Bluesky, X, and Linkedin accounts. In particular, simulation-based inference and ML related techniques will play a crucial role. We are also pleased to announce that Stefan Radev has joined the OWABI Organiser Team.
The first OWABI talk will be given on Thursday the 31st October at 11am UK time. The speaker is Ullrich Koethe (University of Heidelberg), who will talk about
“Free-form flows for physics-informed generative modeling”
Abstract: The talk first introduces a useful categorization of the (sometimes confusing) “generative model zoo” in terms of different change-of-variables formulas. It then shows how normalizing flows, a major architecture for generative neural networks, can be used for simulation-based Bayesian inference in the sciences. Finally, it proposes free-form flows to simplify the incorporation of physical prior knowledge, e.g. rotation and translation invariance or the restriction of the distribution to a manifold, into generative models.
Keywords: normalizing flows; physical-informed neural networks; simulation-based inference
thou shalt not slice thine spaghetti
Posted in Statistics with tags geodesics, manifold, pasta machine, reversibility, Riemann manifold, Riemannian measure, seminar, shrinkage, slice sampler, spaghetti, University of Warwick on May 1, 2024 by xi'an
This 2023 work on Slice sampler on manifolds, as presented in the algorithms seminar in Warwick during a recent visit of by Mareike Hasenpflug, consists in designing and validating slice samplers for distributions on manifolds. It is mildly connected to some current work on MCMC algorithms on manifolds through coupling techniques by [my friends & coauthors] Elena Bortolado, Pierre Jacob, and Robin Ryder (who escape temporarily the manifold at each step). As in Neal (2003), uniform draws from the (super)level sets are replaced there with one-step Markov moves within the level set, that is, slice sampler moves. The slice sampler actually generalises Neal’s (2003) stepping-out and shrinkage steps rather closely. Based on the standard notion of the Riemannian measure induced by the very structure of the manifold, the model therein assumes that the simulation target is available as a closed-form if unormalised density p(x) against that measure, meaning that problems where the distribution is a push-forward one induced by a mapping onto the manifold are not necessary manageable. The slide sampler is decomposed into choosing (1-dimensional) geodesics defined by the manifold (and generalising great circles), uniformly, and then sampling by this one-dimensional slice sampling over the geodesic, under the level set constraint. Meaning that those geodesics must be manageable enough. (Note that the concept of stepping-out does not mean that the chain ever escapes from the manifold.) Demonstrating the validity and reversibility proves a challenging task.
mostly MC[bruary]
Posted in Books, Kids, Statistics, University life with tags completion, contour algorithm, Gibbs sampling, insufficiency, IQR, mad, manifold, MCMC, median, Monte Carlo methods, Monte Carlo Statistical Methods, mostly Monte Carlo seminar, Paris, PariSanté campus, sample median, seminar, The Prairie Chair on February 18, 2024 by xi'an
