Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets. - Yuan Huang; Valentin De Bortoli; Fugen Zhou; Jérôme Gilles (IET Image Process., 2018)

Macrocanonical Models for Texture Synthesis. - Valentin De Bortoli; Agnès Desolneux; Bruno Galerne; Arthur Leclaire (SSVM, 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)

Approximate Bayesian Computation with the Sliced-Wasserstein Distance. - Kimia Nadjahi; Valentin De Bortoli; Alain Durmus; Roland Badeau; Umut Simsekli (ICASSP, 2020)

Quantitative Propagation of Chaos for SGD in Wide Neural Networks. - Valentin De Bortoli; Alain Durmus; Xavier Fontaine; Umut Simsekli (NeurIPS, 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)

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)

Statistiques non locales dans les images: modélisation, estimation et échantillonnage. (Non-local statistics in images: modelisation, estimation and sampling). - Valentin De Bortoli (2020)

Convergence rates and approximation results for SGD and its continuous-time counterpart. - Xavier Fontaine; Valentin De Bortoli; Alain Durmus (COLT, 2021)

Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling. - Valentin De Bortoli; James Thornton; Jeremy Heng; Arnaud Doucet (NeurIPS, 2021)

Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure. - George Deligiannidis; Valentin De Bortoli; Arnaud Doucet (CoRR, 2021)

On quantitative Laplace-type convergence results for some exponential probability measures, with two applications. - Valentin De Bortoli; Agnès Desolneux (CoRR, 2021)

Simulating Diffusion Bridges with Score Matching. - Valentin De Bortoli; Arnaud Doucet; Jeremy Heng; James Thornton (CoRR, 2021)

Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. - Valentin De Bortoli; Alain Durmus; Marcelo Pereyra; Ana Fernandez Vidal (Stat. Comput., 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)

Riemannian Score-Based Generative Modelling. - Valentin De Bortoli; Emile Mathieu; Michael J. Hutchinson; James Thornton; Yee Whye Teh; Arnaud Doucet (NeurIPS, 2022)

A Continuous Time Framework for Discrete Denoising Models. - Andrew Campbell; Joe Benton; Valentin De Bortoli; Thomas Rainforth; George Deligiannidis; Arnaud Doucet (NeurIPS, 2022)

Wavelet Score-Based Generative Modeling. - Florentin Guth; Simon Coste; Valentin De Bortoli; Stéphane Mallat (NeurIPS, 2022)

Can Push-forward Generative Models Fit Multimodal Distributions? - Antoine Salmona; Valentin De Bortoli; Julie Delon; Agnès Desolneux (NeurIPS, 2022)

Conditional simulation using diffusion Schrödinger bridges. - Yuyang Shi 002; Valentin De Bortoli; George Deligiannidis; Arnaud Doucet (UAI, 2022)

Riemannian Score-Based Generative Modeling. - Valentin De Bortoli; Emile Mathieu; Michael J. Hutchinson; James Thornton; Yee Whye Teh; Arnaud Doucet (CoRR, 2022)

Riemannian Diffusion Schrödinger Bridge. - James Thornton; Michael J. Hutchinson; Emile Mathieu; Valentin De Bortoli; Yee Whye Teh; Arnaud Doucet (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)

Spectral Diffusion Processes. - Angus Phillips; Thomas Seror; Michael J. Hutchinson; Valentin De Bortoli; Arnaud Doucet; Emile Mathieu (CoRR, 2022)

Barrier Hamiltonian Monte Carlo. - Maxence Noble; Valentin De Bortoli; Alain Durmus (CoRR, 2022)

From Denoising Diffusions to Denoising Markov Models. - Joe Benton; Yuyang Shi 002; Valentin De Bortoli; George Deligiannidis; Arnaud Doucet (CoRR, 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)

Convergence of denoising diffusion models under the manifold hypothesis. - Valentin De Bortoli (Trans. Mach. Learn. Res., 2022)

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 (ICML, 2023)

Diffusion Schrödinger Bridge Matching. - Yuyang Shi 002; Valentin De Bortoli; Andrew Campbell; Arnaud Doucet (NeurIPS, 2023)

Trans-Dimensional Generative Modeling via Jump Diffusion Models. - Andrew Campbell; William Harvey 002; Christian Weilbach; Valentin De Bortoli; Thomas Rainforth; Arnaud Doucet (NeurIPS, 2023)

Metropolis Sampling for Constrained Diffusion Models. - Nic Fishman; Leo Klarner; Emile Mathieu; Michael J. Hutchinson; Valentin De Bortoli (NeurIPS, 2023)

Geometric Neural Diffusion Processes. - Emile Mathieu; Vincent Dutordoir; Michael J. Hutchinson; Valentin De Bortoli; Yee Whye Teh; Richard E. Turner (NeurIPS, 2023)

Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo. - Maxence Noble; Valentin De Bortoli; Alain Durmus (NeurIPS, 2023)

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. - Maxence Noble; Valentin De Bortoli; Arnaud Doucet; Alain Durmus (NeurIPS, 2023)

Unbalanced Diffusion Schrödinger Bridge. - Matteo Pariset; Ya-Ping Hsieh; Charlotte Bunne; Andreas Krause 001; Valentin De Bortoli (CoRR, 2023)

Linear Convergence Bounds for Diffusion Models via Stochastic Localization. - Joe Benton; Valentin De Bortoli; Arnaud Doucet; George Deligiannidis (CoRR, 2023)

Augmented Bridge Matching. - Valentin De Bortoli; Guan-Horng Liu; Tianrong Chen; Evangelos A. Theodorou; Weilie Nie (CoRR, 2023)

Diffusion Models for Constrained Domains. - Nic Fishman; Leo Klarner; Valentin De Bortoli; Emile Mathieu; Michael John Hutchinson (Trans. Mach. Learn. Res., 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., 2023)

Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization. - Joe Benton; Valentin De Bortoli; Arnaud Doucet; George Deligiannidis (ICLR, 2024)

Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models. - Gabriele Corso; Yilun Xu; Valentin De Bortoli; Regina Barzilay; Tommi S. Jaakkola (ICLR, 2024)

Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models. - Marien Renaud; Jiaming Liu 001; Valentin De Bortoli; Andrés Almansa; Ulugbek Kamilov (ICLR, 2024)

Particle Denoising Diffusion Sampler. - Angus Phillips; Hai-Dang Dau; Michael John Hutchinson; Valentin De Bortoli; George Deligiannidis; Arnaud Doucet (ICML, 2024)

Schrodinger Bridge Flow for Unpaired Data Translation. - Valentin De Bortoli; Iryna Korshunova; Andriy Mnih; Arnaud Doucet (NeurIPS, 2024)

Target Score Matching. - Valentin De Bortoli; Michael J. Hutchinson; Peter Wirnsberger; Arnaud Doucet (CoRR, 2024)

Dynamical Regimes of Diffusion Models. - Giulio Biroli; Tony Bonnaire; Valentin De Bortoli; Marc Mézard (CoRR, 2024)

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. - Lingkai Kong; Yuanqi Du; Wenhao Mu; Kirill Neklyudov; Valentin De Bortoli; Dongxia Wu; Haorui Wang; Aaron M. Ferber; Yian Ma; Carla P. Gomes; Chao Zhang 014 (AISTATS, 2025)

Implicit Diffusion: Efficient optimization through stochastic sampling. - Pierre Marion; Anna Korba; Peter L. Bartlett; Mathieu Blondel; Valentin De Bortoli; Arnaud Doucet; Felipe Llinares-López; Courtney Paquette; Quentin Berthet (AISTATS, 2025)

Deep MMD Gradient Flow without adversarial training. - Alexandre Galashov; Valentin De Bortoli; Arthur Gretton (ICLR, 2025)

Accelerated Diffusion Models via Speculative Sampling. - Valentin De Bortoli; Alexandre Galashov; Arthur Gretton; Arnaud Doucet (CoRR, 2025)

Distributional Diffusion Models with Scoring Rules. - Valentin De Bortoli; Alexandre Galashov; J. Swaroop Guntupalli; Guangyao Zhou; Kevin Murphy 002; Arthur Gretton; Arnaud Doucet (CoRR, 2025)

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling. - Marien Renaud; Valentin De Bortoli; Arthur Leclaire; Nicolas Papadakis (CoRR, 2025)

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities. - Tara Akhound-Sadegh; Jungyoon Lee; Avishek Joey Bose; Valentin De Bortoli; Arnaud Doucet; Michael M. Bronstein; Dominique Beaini; Siamak Ravanbakhsh; Kirill Neklyudov; Alexander Tong 001 (CoRR, 2025)