Archive for Université Paris Dauphine

ISBA³

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , on July 2, 2026 by xi'an

After another absurdly early run along the Sendai River, in higher humidity than yesterday, I had plenty of time to have breakfast and commute to the conference centre for our privacy section. (“Ours” since I organised and chaired the session, but also all speakers were related with our ERC OCEAN project.) With talks by Antoine Luciano (Dauphine), Shenggang Hu (Warwick) and Joshua Bon (formerly Dauphine), Shenggang presenting his work with Gareth on calibrating a DP, noisy, unbiased, version of Metropolis-Hastings. Given the other sessions highly competing with ours, this 100% OCEAN session was well-attended! My second morning session was the theory & method part of the Savage Award, with unfortunately two of the speakers stuck in the US for visa issues and presenting remotely. There have been years where this session suffered from competition from parallel sessions, but this time showed a quite decent attendance.

As most members of the scientific committee of the BayesComp mirror in Aussois were present, we went out for lunch at a nearby hitsumabushi restaurant to plan registrations and local sessions. Which proved very productive despite enjoying the fantastic eel dishes! If making me miss the beginning of the afternoon session… and then the whole session as the room on predictive Bayes was packed (and some speakers had already delivered on the Isle of Skye). I managed to get to Bottond Szabo’s Foundation lecture, with again common threads with Skye, and then returned to my rental as I was quickly crashing…

IUF congrats!

Posted in pictures, University life with tags , , , , , , on June 16, 2026 by xi'an

optimal sampling for kernel quadrature on unbounded domains

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , on May 22, 2026 by xi'an

My PhD student Edoardo Bandoni, along with Julien Stoehr and myself, completed a paper on validating (Bayesian) kernel quadrature with unbounded domains of integration. Which connects with probabilistic numerics, since the integrand is modelled as a Gaussian process. And RKHS methods. As opposed to Monte Carlo estimators, quadrature methods approximate integrals of smooth functions with worst-case error decaying at a minimax rate α/d for smoothness α in dimension d. Existing rate-optimal quadrature methods often depend on deterministic point sets tailored to a specific kernel, making them sensitive to misspecification and thus less robust in practice. This paper studies instead randomised quadrature methods, with a focus on robustness rather than on kernel-specific optimality. We construct an explicit, n-dependent, sampling distribution that achieves minimax rates for worst-case errors over smoothness classes without requiring knowledge of the kernel. This kernel-agnostic design does improve robustness while retaining optimal rates and extends Briol et al.  (2019) to the unbounded case. Which cannot always be easily handled by a change of variables. Our result thus mostly covers unbounded sampling measures such as Gaussian and Student-t distributions, extending beyond compact domains. The results provide both theoretical guarantees and a practical recipe for robust, rate-optimal, randomised quadrature. [The above is mostly stated in the abstract.]

Cristina Toninelli, CEREMADE CNRS silver medallist [congrats!]

Posted in pictures, University life with tags , , , , , , , , , , , on April 29, 2026 by xi'an

incoming mostly Monte Carlo [14 April, PariSanté campus]

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , , , , , on April 9, 2026 by xi'an

The next Mostly Monte Carlo seminar will be this very Friday, 10/04/26, at PariSanté Campus. With Shiva Darshan and Pierre Monmarché speaking on the following topics:
15h: Shiva Darshan Maximal-reflection couplings on manifolds: some specific examples
Explicit Markovian couplings can be used to build Markov Chain Monte Carlo methods such unbiased MCMC or coupling based control variates. For sampling from probability measures supported on Euclidean space, one typically uses a synchronous coupling, a maximal-reflection coupling (also known as a discrete-time sticky coupling), or some variant of the two. For probability measures supported on Riemannian manifolds, the situation is less clear cut. While the Kendall-Cranston coupling of Brownian motions on manifolds has been successfully applied in theoretical works, it is ill-suited for building explicit algorithms. In this talk, we will discuss some of the obstacles to extending Euclidean maximal-reflection couplings to manifolds and present some special cases for which these obstacles can be easily overcome. With applications to Stereographic MCMC in mind, we detail particular couplings of random walks on the sphere.
16h: Pierre Monmarché A post-sampling reweighting method for multi-modal target measures
Even when the modes are identified and sampled locally with MCMC methods, a difficulty to sample multi-modal measures is to correctly estimate the relative probabilities of each of these modes, which requires to observe many transitions between them (which are rare events). We will present an approach based on variational inference which exploits the local samples, aiming only at estimating the relative weights between them. When the modes are well separated, this amount to some entropy estimations.