Archive for seminar
seminari di scienza statistiche a Padova
Posted in Books, Statistics, Travel, University life with tags Bayesian Adversarial Privacy, Italy, Padova, seminar, Università Ca' Foscari Venezia, Università degli studi di Padova, Venezia, visiting position on April 2, 2026 by xi'an
mostly Monte Carlo [13/03]
Posted in Statistics, Travel, University life with tags #ERCSyG, Adam, ERC, Flatiron Institute, Gibbs sampler, Hamiltonian Monte Carlo, HMC, INRIA, Kantorovich semi-distances, Langevin diffusion, Markov kernel, Markov semigroup, MCMC, NUTS, Ocean, Paris, PariSanté campus, seminar, WALNUTS on March 10, 2026 by xi'an
A new episode of our mostly Monte Carlo seminar, very soon coming near you (if in Paris):
On Friday 13/02/26, from 3-5pm at PariSanté Campus
15h00: Pierre Del Moral (INRIA, Bordeaux)
On the Kantorovich contraction of Markov semigroup
We present a novel operator theoretic framework to study the contraction properties of Markov semigroups with respect to a general class of Kantorovich semi-distances, which notably includes Wasserstein distances. This rather simple contraction cost framework combines standard Lyapunov techniques with local contraction conditions. Our results can be applied to both discrete time and continuous time Markov semigroups, and we illustrate their wide applicability in the context of (i) Markov transitions on models with boundary states, including bounded domains with entrance boundaries, (ii) operator products of a Markov kernel and its adjoint, including two-block-type Gibbs samplers, (iii) iterated random functions and (iv) diffusion models, including overdampted Langevin diffusion with convex at infinity potentials.
16h00: Bob Carpenter (Flatiron Institute, New York)
GIST, WALNUTS, and Continuous Nutpie: mass-matrix and step-size adaptation for Hamiltonian Monte Carlo
I will introduce Gibbs self tuning (GIST), our new technique for coupling tuning parameters and conditionally Gibbs-sampling them per iteration in Hamiltonian Monte Carlo. Then I will turn to the within-orbit adaptive NUTS (WALNUTS) sampler, which adapts the step size every leapfrog step in order to conserve the Hamiltonian. Empirical evaluations on varying multi-scale target distributions, including Neal’s funnel and the Stock-Watson stochastic volatility time-series model, demonstrate that WALNUTS achieves substantial improvements in sampling efficiency and robustness. I will review the Nutpie mass-matrix adaptation scheme, which is designed to minimize Fisher divergence by estimating the mass matrix as the geometric midpoint (aka barycenter) between the inverse covariance of the draws and the covariance of the scores of the draws. Then I will describe a continuously adapting version that adapts per iteration by continuously discounting the past rather than updating in fixed blocks. I will also show how the Adam optimizer outperforms dual averaging for step-size adaptation. I will conclude by considering a lock-free multi-threading implementation that automatically monitors adaptation and sampling for convergence for automatic stopping.
mostly Monte Carlo [20/02]
Posted in Statistics with tags #ERCSyG, École Polytechnique, ERC, federated learning, generative prior, Gibbs sampling, inverse problems, machine learning model, MCMC, Ocean, Palaiseau, Paris, PariSanté campus, privacy, Scaffold algorithm, seminar, stochastic gradient on February 16, 2026 by xi'an
A new episode of our mostly Monte Carlo seminar, very soon coming near you (if in Paris):
On Friday 20/02/26, from 3-5pm at PariSanté Campus
15h: Paul Mangold (École Polytechnique, Palaiseau)
Convergence and Linear Speed-Up in Stochastic Federated Learning
In federated learning, multiple users collaboratively train a machine learning model without sharing local data. To reduce communication, users perform multiple local stochastic gradient steps that are then aggregated by a central server. However, due to data heterogeneity, local training introduces bias. In this talk, I will present a novel interpretation of the Federated Averaging algorithm, establishing its convergence to a stationary distribution. By analyzing this distribution, we show that the bias consists of two components: one due to heterogeneity and another due to gradient stochasticity. I will then extend this analysis to the Scaffold algorithm, demonstrating that it effectively mitigates heterogeneity bias but not stochasticity bias. Finally, we show that both algorithms achieve linear speed-up in the number of agents, a key property in federated stochastic optimization.
16h: Alain Durmus (École Polytechnique, Palaiseau)
A Mixture-based Framework for Guiding Diffusion Models
Inverse problems—such as image restoration from noisy or incomplete measurements and musical source separation—are ill-posed, making Bayesian approaches with learned generative priors especially appealing. Diffusion models provide powerful priors, but existing posterior sampling methods often rely on crude likelihood-gradient approximations and heavy task-specific tuning. In this talk, I will introduce a novel principled approach specifically designed to overcome these limitations. The core contribution of this approach is the construction of a mixture approximation of intermediate posterior distributions defined by the diffusion model. The sampling is carried out sequentially via Gibbs sampling, a Markov Chain Monte Carlo method, using a careful data augmentation scheme. Gibbs sampling is employed here due to its simplicity and theoretical guarantees, allowing for exact conditional updates at each iteration, thus ensuring stability and efficiency. One key advantage of the presented algorithm is its flexibility: it adapts to varying levels of computational resources by adjusting the number of Gibbs iterations. Consequently, substantial performance gains can be achieved by increasing inference-time computational effort. I will present extensive experimental results demonstrating empirical performance across diverse image restoration tasks, involving both pixel-space and latent-space diffusion models, and showcase its successful application in musical source separation.
destinazione bellissima Roma
Posted in Kids, pictures, Running, Travel, University life with tags fori romani, IASD Master programme, Italy, La Sapienza, Master program, Roma, Rome, seminar, Université Paris Dauphine on January 18, 2026 by xi'an
January session of the mostly Monte Carlo seminar (16/01, 3pm)
Posted in Statistics, University life with tags #ERCSyG, HMC, Monte Carlo Statistical Methods, mostly Monte Carlo seminar, multi-armed bandits, Ocean, Paris, PariSanté campus, Porte de Versailles, privacy, seminar, stereographic MCMC on January 9, 2026 by xi'an
