Valentin De Bortoli

According to our database, Valentin De Bortoli authored at least 48 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Dynamical Regimes of Diffusion Models.

Giulio Biroli, Tony Bonnaire, Valentin De Bortoli, Marc Mézard

CoRR, 2024

Target Score Matching.

Valentin De Bortoli, Michael J. Hutchinson, Peter Wirnsberger, Arnaud Doucet

CoRR, 2024

Particle Denoising Diffusion Sampler.

Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet

CoRR, 2024

Implicit Diffusion: Efficient Optimization through Stochastic Sampling.

Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet

CoRR, 2024

2023

On Maximum a Posteriori Estimation with Plug & Play Priors and Stochastic Gradient Descent.

Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra

J. Math. Imaging Vis., January, 2023

Augmented Bridge Matching.

Valentin De Bortoli, Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou, Weilie Nie

CoRR, 2023

Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models.

Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi S. Jaakkola

CoRR, 2023

Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models.

Marien Renaud, Jiaming Liu, Valentin De Bortoli, Andrés Almansa, Ulugbek S. Kamilov

CoRR, 2023

Linear Convergence Bounds for Diffusion Models via Stochastic Localization.

Joe Benton, Valentin De Bortoli, Arnaud Doucet, George Deligiannidis

CoRR, 2023

Metropolis Sampling for Constrained Diffusion Models.

Nic Fishman, Leo Klarner, Emile Mathieu, Michael J. Hutchinson, Valentin De Bortoli

CoRR, 2023

Geometric Neural Diffusion Processes.

Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner

CoRR, 2023

Unbalanced Diffusion Schrödinger Bridge.

Matteo Pariset, Ya-Ping Hsieh, Charlotte Bunne, Andreas Krause, Valentin De Bortoli

CoRR, 2023

Diffusion Models for Constrained Domains.

Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson

CoRR, 2023

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters.

Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo.

Maxence Noble, Valentin De Bortoli, Alain Durmus

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Geometric Neural Diffusion Processes.

Emile Mathieu, Vincent Dutordoir, Michael Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Metropolis Sampling for Constrained Diffusion Models.

Nic Fishman, Leo Klarner, Emile Mathieu, Michael Hutchinson, Valentin De Bortoli

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Trans-Dimensional Generative Modeling via Jump Diffusion Models.

Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Thomas Rainforth, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Diffusion Schrödinger Bridge Matching.

Yuyang Shi, Valentin De Bortoli, Andrew Campbell, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola

Proceedings of the International Conference on Machine Learning, 2023

2022

Convergence of denoising diffusion models under the manifold hypothesis.

Valentin De Bortoli

Trans. Mach. Learn. Res., 2022

Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie.

Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra

SIAM J. Imaging Sci., 2022

From Denoising Diffusions to Denoising Markov Models.

Joe Benton, Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet

CoRR, 2022

Barrier Hamiltonian Monte Carlo.

Maxence Noble, Valentin De Bortoli, Alain Durmus

CoRR, 2022

Spectral Diffusion Processes.

Angus Phillips, Thomas Seror, Michael J. Hutchinson, Valentin De Bortoli, Arnaud Doucet, Emile Mathieu

CoRR, 2022

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

Francesca R. Crucinio, Valentin De Bortoli, Arnaud Doucet, Adam M. Johansen

CoRR, 2022

Riemannian Diffusion Schrödinger Bridge.

James Thornton, Michael J. Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet

CoRR, 2022

Riemannian Score-Based Generative Modeling.

Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet

CoRR, 2022

Conditional simulation using diffusion Schrödinger bridges.

Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Can Push-forward Generative Models Fit Multimodal Distributions?

Antoine Salmona, Valentin De Bortoli, Julie Delon, Agnès Desolneux

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Wavelet Score-Based Generative Modeling.

Florentin Guth, Simon Coste, Valentin De Bortoli, Stéphane Mallat

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Continuous Time Framework for Discrete Denoising Models.

Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Riemannian Score-Based Generative Modelling.

Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Maximum Entropy Methods for Texture Synthesis: Theory and Practice.

Valentin De Bortoli, Agnès Desolneux, Alain Durmus, Bruno Galerne, Arthur Leclaire

SIAM J. Math. Data Sci., 2021

Efficient stochastic optimisation by unadjusted Langevin Monte Carlo.

Valentin De Bortoli, Alain Durmus, Marcelo Pereyra, Ana Fernandez Vidal

Stat. Comput., 2021

Simulating Diffusion Bridges with Score Matching.

Valentin De Bortoli, Arnaud Doucet, Jeremy Heng, James Thornton

CoRR, 2021

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

Valentin De Bortoli, Agnès Desolneux

CoRR, 2021

Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure.

George Deligiannidis, Valentin De Bortoli, Arnaud Doucet

CoRR, 2021

Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling.

Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Convergence rates and approximation results for SGD and its continuous-time counterpart.

Xavier Fontaine, Valentin De Bortoli, Alain Durmus

Proceedings of the Conference on Learning Theory, 2021

2020

Statistiques non locales dans les images: modélisation, estimation et échantillonnage. (Non-local statistics in images: modelisation, estimation and sampling).

Valentin De Bortoli

PhD thesis, 2020

Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments.

Ana Fernandez Vidal, Valentin De Bortoli, Marcelo Pereyra, Alain Durmus

SIAM J. Imaging Sci., 2020

Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis.

Valentin De Bortoli, Alain Durmus, Marcelo Pereyra, Ana Fernandez Vidal

SIAM J. Imaging Sci., 2020

Quantitative Propagation of Chaos for SGD in Wide Neural Networks.

Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Approximate Bayesian Computation with the Sliced-Wasserstein Distance.

Kimia Nadjahi, Valentin De Bortoli, Alain Durmus, Roland Badeau, Umut Simsekli

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

Patch Redundancy in Images: A Statistical Testing Framework and Some Applications.

Valentin De Bortoli, Agnès Desolneux, Bruno Galerne, Arthur Leclaire

SIAM J. Imaging Sci., 2019

Macrocanonical Models for Texture Synthesis.

Valentin De Bortoli, Agnès Desolneux, Bruno Galerne, Arthur Leclaire

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2019

2018

Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets.

Yuan Huang, Valentin De Bortoli, Fugen Zhou, Jérôme Gilles

IET Image Process., 2018