Xi'an's Og

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

This entry was posted on May 12, 2026 at 12:26 am and is filed under 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. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.