Valentin De Bortoli

research

∙ 08/27/2023

Diffusion Schrödinger Bridges for Bayesian Computation

Denoising diffusion models are a novel class of generative models that h...

0 Jeremy Heng, et al. ∙

research

∙ 08/07/2023

Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Diffusion models are a powerful method for generating approximate sample...

0 Joe Benton, et al. ∙

research

∙ 07/11/2023

Metropolis Sampling for Constrained Diffusion Models

Denoising diffusion models have recently emerged as the predominant para...

0 Nic Fishman, et al. ∙

research

∙ 07/11/2023

Geometric Neural Diffusion Processes

Denoising diffusion models have proven to be a flexible and effective pa...

0 Emile Mathieu, et al. ∙

research

∙ 06/15/2023

Unbalanced Diffusion Schrödinger Bridge

Schrödinger bridges (SBs) provide an elegant framework for modeling the ...

0 Matteo Pariset, 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

∙ 05/25/2023

Trans-Dimensional Generative Modeling via Jump Diffusion Models

We propose a new class of generative models that naturally handle data o...

0 Andrew Campbell, et al. ∙

research

∙ 04/11/2023

Diffusion Models for Constrained Domains

Denoising diffusion models are a recent class of generative models which...

0 Nic Fishman, et al. ∙

research

∙ 03/29/2023

Diffusion Schrödinger Bridge Matching

Solving transport problems, i.e. finding a map transporting one given di...

0 Yuyang Shi, et al. ∙

research

∙ 02/05/2023

SE(3) diffusion model with application to protein backbone generation

The design of novel protein structures remains a challenge in protein en...

0 Jason Yim, et al. ∙

research

∙ 11/07/2022

From Denoising Diffusions to Denoising Markov Models

Denoising diffusions are state-of-the-art generative models which exhibi...

0 Joe Benton, 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

∙ 09/28/2022

Spectral Diffusion Processes

Score-based generative modelling (SGM) has proven to be a very effective...

8 Angus Phillips, et al. ∙

research

∙ 09/16/2022

Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows

Solving Fredholm equations of the first kind is crucial in many areas of...

0 Francesca R Crucinio, et al. ∙

research

∙ 08/10/2022

Convergence of denoising diffusion models under the manifold hypothesis

Denoising diffusion models are a recent class of generative models exhib...

0 Valentin De Bortoli, et al. ∙

research

∙ 08/09/2022

Wavelet Score-Based Generative Modeling

Score-based generative models (SGMs) synthesize new data samples from Ga...

9 Florentin Guth, et al. ∙

research

∙ 07/07/2022

Riemannian Diffusion Schrödinger Bridge

Score-based generative models exhibit state of the art performance on de...

32 James Thornton, et al. ∙

research

∙ 06/29/2022

Can Push-forward Generative Models Fit Multimodal Distributions?

Many generative models synthesize data by transforming a standard Gaussi...

0 Antoine Salmona, et al. ∙

research

∙ 05/30/2022

A Continuous Time Framework for Discrete Denoising Models

We provide the first complete continuous time framework for denoising di...

0 Andrew Campbell, et al. ∙

research

∙ 02/27/2022

Conditional Simulation Using Diffusion Schrödinger Bridges

Denoising diffusion models have recently emerged as a powerful class of ...

0 Yuyang Shi, et al. ∙

research

∙ 02/06/2022

Riemannian Score-Based Generative Modeling

Score-based generative models (SGMs) are a novel class of generative mod...

5 Valentin De Bortoli, 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

∙ 11/14/2021

Simulating Diffusion Bridges with Score Matching

We consider the problem of simulating diffusion bridges, i.e. diffusion ...

9 Valentin De Bortoli, et al. ∙

research

∙ 10/25/2021

On quantitative Laplace-type convergence results for some exponential probability measures, with two applications

Laplace-type results characterize the limit of sequence of measures (π_ε...

0 Valentin De Bortoli, et al. ∙

research

∙ 08/18/2021

Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure

We establish the uniform in time stability, w.r.t. the marginals, of the...

0 George Deligiannidis, et al. ∙

research

∙ 06/01/2021

Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling

Progressively applying Gaussian noise transforms complex data distributi...

21 Valentin De Bortoli, 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

∙ 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

∙ 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

∙ 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

∙ 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. ∙

Valentin De Bortoli

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