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...
share
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...
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...
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 ...
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 ...
research
∙
03/10/2023
Rosenthal-type inequalities for linear statistics of Markov chains
In this paper, we establish novel deviation bounds for additive function...
research
∙
02/09/2023
On Sampling with Approximate Transport Maps
Transport maps can ease the sampling of distributions with non-trivial g...
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...
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...
research
∙
10/21/2022
Barrier Hamiltonian Monte Carlo
In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a vers...
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...
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...
research
∙
06/07/2022
FedPop: A Bayesian Approach for Personalised Federated Learning
Personalised federated learning (FL) aims at collaboratively learning a ...
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...
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...
research
∙
01/06/2022
On the geometric convergence for MALA under verifiable conditions
While the Metropolis Adjusted Langevin Algorithm (MALA) is a popular and...
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...
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...
research
∙
06/30/2021
Monte Carlo Variational Auto-Encoders
Variational auto-encoders (VAE) are popular deep latent variable models ...
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...
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...
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...
research
∙
06/01/2021
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
Federated learning aims at conducting inference when data are decentrali...
research
∙
04/14/2021
Discrete sticky couplings of functional autoregressive processes
In this paper, we provide bounds in Wasserstein and total variation dist...
research
∙
03/17/2021
Invertible Flow Non Equilibrium sampling
Simultaneously sampling from a complex distribution with intractable nor...
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 (...
research
∙
02/15/2021
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size
This paper studies fixed step-size stochastic approximation (SA) schemes...
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...
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...
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...
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...
research
∙
05/27/2020
Convergence Analysis of Riemannian Stochastic Approximation Schemes
This paper analyzes the convergence for a large class of Riemannian stoc...
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...
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...
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 ...
research
∙
12/03/2019
Maximum entropy methods for texture synthesis: theory and practice
Recent years have seen the rise of convolutional neural network techniqu...
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...
research
∙
10/28/2019
Approximate Bayesian Computation with the Sliced-Wasserstein Distance
Approximate Bayesian Computation (ABC) is a popular method for approxima...
research
∙
07/10/2019
Markov Decision Process for MOOC users behavioral inference
Studies on massive open online courses (MOOCs) users discuss the existen...
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...
research
∙
06/11/2019
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Minimum expected distance estimation (MEDE) algorithms have been widely ...
research
∙
04/15/2019
Copula-like Variational Inference
This paper considers a new family of variational distributions motivated...
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...
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...
research
∙
08/26/2018
Hypercoercivity of Piecewise Deterministic Markov Process-Monte Carlo
In this paper we derive spectral gap estimates for several Piecewise Det...
research
∙
08/05/2018
Diffusion approximations and control variates for MCMC
A new methodology is presented for the construction of control variates ...
research
∙
07/14/2018
Piecewise Deterministic Markov Processes and their invariant measure
Piecewise Deterministic Markov Processes (PDMPs) are studied in a genera...
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...
research
∙
02/26/2018
Analysis of Langevin Monte Carlo via convex optimization
In this paper, we provide new insights on the Unadjusted Langevin Algori...
research
∙
07/20/2017