b'Alain Durmus'

research

∙ 08/23/2023

Score diffusion models without early stopping: finite Fisher information is all you need

Diffusion models are a new class of generative models that revolve aroun...

0 Giovanni Conforti, et al. ∙

research

∙ 07/19/2023

VITS : Variational Inference Thomson Sampling for contextual bandits

In this paper, we introduce and analyze a variant of the Thompson sampli...

0 Pierre Clavier, et al. ∙

research

∙ 07/07/2023

On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers

There is substantial empirical evidence about the success of dynamic imp...

0 Alain Durmus, et al. ∙

research

∙ 05/26/2023

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters

Multi-marginal Optimal Transport (mOT), a generalization of OT, aims at ...

0 Maxence Noble, et al. ∙

research

∙ 04/13/2023

Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach

Computational optimal transport (OT) has recently emerged as a powerful ...

0 Giacomo Greco, et al. ∙

research

∙ 03/10/2023

Rosenthal-type inequalities for linear statistics of Markov chains

In this paper, we establish novel deviation bounds for additive function...

0 Alain Durmus, et al. ∙

research

∙ 02/09/2023

On Sampling with Approximate Transport Maps

Transport maps can ease the sampling of distributions with non-trivial g...

0 Louis Grenioux, et al. ∙

research

∙ 01/06/2023

Optimal Scaling Results for a Wide Class of Proximal MALA Algorithms

We consider a recently proposed class of MCMC methods which uses proximi...

0 Francesca R Crucinio, et al. ∙

research

∙ 10/31/2022

Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms

This paper focuses on Bayesian inference in a federated learning context...

0 Vincent Plassier, et al. ∙

research

∙ 10/21/2022

Barrier Hamiltonian Monte Carlo

In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a vers...

0 Maxence Noble, et al. ∙

research

∙ 07/10/2022

Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation

This paper provides a finite-time analysis of linear stochastic approxim...

0 Alain Durmus, et al. ∙

research

∙ 07/08/2022

Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study

This paper studies the Variational Inference (VI) used for training Baye...

0 Tom Huix, et al. ∙

research

∙ 06/07/2022

FedPop: A Bayesian Approach for Personalised Federated Learning

Personalised federated learning (FL) aims at collaboratively learning a ...

0 Nikita Kotelevskii, et al. ∙

research

∙ 01/16/2022

On Maximum-a-Posteriori estimation with Plug Play priors and stochastic gradient descent

Bayesian methods to solve imaging inverse problems usually combine an ex...

7 Rémi Laumont, et al. ∙

research

∙ 01/13/2022

Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm

The present paper focuses on the problem of sampling from a given target...

0 Randal Douc, et al. ∙

research

∙ 01/06/2022

On the geometric convergence for MALA under verifiable conditions

While the Metropolis Adjusted Langevin Algorithm (MALA) is a popular and...

0 Alain Durmus, et al. ∙

research

∙ 08/02/2021

Asymptotic bias of inexact Markov Chain Monte Carlo methods in high dimension

This paper establishes non-asymptotic bounds on Wasserstein distances be...

0 Alain Durmus, et al. ∙

research

∙ 07/30/2021

Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics

We study the convergence in total variation and V-norm of discretization...

0 Alain Durmus, et al. ∙

research

∙ 06/30/2021

Monte Carlo Variational Auto-Encoders

Variational auto-encoders (VAE) are popular deep latent variable models ...

0 Achille Thin, et al. ∙

research

∙ 06/29/2021

Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections

The Sliced-Wasserstein distance (SW) is being increasingly used in machi...

0 Kimia Nadjahi, et al. ∙

research

∙ 06/11/2021

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs

Performing reliable Bayesian inference on a big data scale is becoming a...

0 Vincent Plassier, et al. ∙

research

∙ 06/02/2021

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

This paper provides a non-asymptotic analysis of linear stochastic appro...

0 Alain Durmus, et al. ∙

research

∙ 06/01/2021

QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning

Federated learning aims at conducting inference when data are decentrali...

0 Maxime Vono, et al. ∙

research

∙ 04/14/2021

Discrete sticky couplings of functional autoregressive processes

In this paper, we provide bounds in Wasserstein and total variation dist...

0 Alain Durmus, et al. ∙

research

∙ 03/17/2021

Invertible Flow Non Equilibrium sampling

Simultaneously sampling from a complex distribution with intractable nor...

0 Achille Thin, et al. ∙

research

∙ 03/08/2021

Bayesian imaging using Plug Play priors: when Langevin meets Tweedie

Since the seminal work of Venkatakrishnan et al. (2013), Plug Play (...

10 Rémi Laumont, et al. ∙

research

∙ 02/15/2021

On Riemannian Stochastic Approximation Schemes with Fixed Step-Size

This paper studies fixed step-size stochastic approximation (SA) schemes...

0 Alain Durmus, et al. ∙

research

∙ 01/30/2021

On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning

This paper studies the exponential stability of random matrix products d...

0 Alain Durmus, et al. ∙

research

∙ 12/31/2020

Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees

Markov Chain Monte Carlo (MCMC) is a class of algorithms to sample compl...

0 Achille Thin, et al. ∙

research

∙ 08/13/2020

Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part II: Theoretical Analysis

This paper presents a detailed theoretical analysis of the three stochas...

0 Valentin De Bortoli, et al. ∙

research

∙ 07/13/2020

Quantitative Propagation of Chaos for SGD in Wide Neural Networks

In this paper, we investigate the limiting behavior of a continuous-time...

0 Valentin De Bortoli, et al. ∙

research

∙ 05/27/2020

Convergence Analysis of Riemannian Stochastic Approximation Schemes

This paper analyzes the convergence for a large class of Riemannian stoc...

0 Alain Durmus, et al. ∙

research

∙ 04/08/2020

Continuous and Discrete-Time Analysis of Stochastic Gradient Descent for Convex and Non-Convex Functions

This paper proposes a thorough theoretical analysis of Stochastic Gradie...

0 Xavier Fontaine, et al. ∙

research

∙ 03/12/2020

Statistical and Topological Properties of Sliced Probability Divergences

The idea of slicing divergences has been proven to be successful when co...

0 Kimia Nadjahi, et al. ∙

research

∙ 02/27/2020

MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference

In this contribution, we propose a new computationally efficient method ...

76 Achille Thin, et al. ∙

research

∙ 12/03/2019

Maximum entropy methods for texture synthesis: theory and practice

Recent years have seen the rise of convolutional neural network techniqu...

20 Valentin De Bortoli, et al. ∙

research

∙ 11/26/2019

Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach

Many imaging problems require solving an inverse problem that is ill-con...

0 Ana F. Vidal, et al. ∙

research

∙ 10/28/2019

Approximate Bayesian Computation with the Sliced-Wasserstein Distance

Approximate Bayesian Computation (ABC) is a popular method for approxima...

0 Kimia Nadjahi, et al. ∙

research

∙ 07/10/2019

Markov Decision Process for MOOC users behavioral inference

Studies on massive open online courses (MOOCs) users discuss the existen...

0 Firas Jarboui, et al. ∙

research

∙ 06/28/2019

Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation

Stochastic approximation methods play a central role in maximum likeliho...

0 Valentin De Bortoli, et al. ∙

research

∙ 06/11/2019

Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance

Minimum expected distance estimation (MEDE) algorithms have been widely ...

0 Kimia Nadjahi, et al. ∙

research

∙ 04/15/2019

Copula-like Variational Inference

This paper considers a new family of variational distributions motivated...

0 Marcel Hirt, et al. ∙

research

∙ 11/25/2018

The promises and pitfalls of Stochastic Gradient Langevin Dynamics

Stochastic Gradient Langevin Dynamics (SGLD) has emerged as a key MCMC a...

0 Nicolas Brosse, et al. ∙

research

∙ 08/26/2018

Hypocoercivity of Piecewise Deterministic Markov Process-Monte Carlo

In this work, we establish L^2-exponential convergence for a broad class...

0 Christophe Andrieu, et al. ∙

research

∙ 08/26/2018

Hypercoercivity of Piecewise Deterministic Markov Process-Monte Carlo

In this paper we derive spectral gap estimates for several Piecewise Det...

0 Christophe Andrieu, et al. ∙

research

∙ 08/05/2018

Diffusion approximations and control variates for MCMC

A new methodology is presented for the construction of control variates ...

0 Nicolas Brosse, et al. ∙

research

∙ 07/14/2018

Piecewise Deterministic Markov Processes and their invariant measure

Piecewise Deterministic Markov Processes (PDMPs) are studied in a genera...

0 Alain Durmus, et al. ∙

research

∙ 06/21/2018

Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions

By building up on the recent theory that established the connection betw...

0 Umut Şimşekli, et al. ∙

research

∙ 02/26/2018

Analysis of Langevin Monte Carlo via convex optimization

In this paper, we provide new insights on the Unadjusted Langevin Algori...

0 Alain Durmus, et al. ∙

research

∙ 07/20/2017

Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains

We consider the minimization of an objective function given access to un...

0 Aymeric Dieuleveut, et al. ∙

Alain Durmus

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