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Valentin De Bortoli
Valentin De Bortoli
Google DeepMind, London
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Diffusion Schr÷dinger bridge with applications to score-based generative modeling
V De Bortoli, J Thornton, J Heng, A Doucet
Advances in Neural Information Processing Systems 34, 17695-17709, 2021
2222021
De novo design of protein structure and function with RFdiffusion
JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, ...
Nature 620 (7976), 1089-1100, 2023
1912023
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, ...
BioRxiv, 2022.12. 09.519842, 2022
1372022
Riemannian score-based generative modelling
V De Bortoli, E Mathieu, M Hutchinson, J Thornton, YW Teh, A Doucet
Advances in Neural Information Processing Systems 35, 2406-2422, 2022
932022
Bayesian imaging using plug & play priors: when langevin meets tweedie
R Laumont, VD Bortoli, A Almansa, J Delon, A Durmus, M Pereyra
SIAM Journal on Imaging Sciences 15 (2), 701-737, 2022
782022
Convergence of denoising diffusion models under the manifold hypothesis
V De Bortoli
arXiv preprint arXiv:2208.05314, 2022
672022
SE (3) diffusion model with application to protein backbone generation
J Yim, BL Trippe, V De Bortoli, E Mathieu, A Doucet, R Barzilay, ...
arXiv preprint arXiv:2302.02277, 2023
622023
Maximum likelihood estimation of regularization parameters in high-dimensional inverse problems: An empirical bayesian approach part i: Methodology and experiments
AF Vidal, V De Bortoli, M Pereyra, A Durmus
SIAM Journal on Imaging Sciences 13 (4), 1945-1989, 2020
502020
Conditional simulation using diffusion Schr÷dinger bridges
Y Shi, V De Bortoli, G Deligiannidis, A Doucet
Uncertainty in Artificial Intelligence, 1792-1802, 2022
392022
A continuous time framework for discrete denoising models
A Campbell, J Benton, V De Bortoli, T Rainforth, G Deligiannidis, ...
Advances in Neural Information Processing Systems 35, 28266-28279, 2022
332022
Approximate Bayesian computation with the sliced-Wasserstein distance
K Nadjahi, V De Bortoli, A Durmus, R Badeau, U Şimşekli
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech andá…, 2020
322020
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo: Application to maximum marginal likelihood and empirical Bayesian estimation
V De Bortoli, A Durmus, M Pereyra, AF Vidal
Statistics and Computing 31, 1-18, 2021
30*2021
Wavelet score-based generative modeling
F Guth, S Coste, V De Bortoli, S Mallat
Advances in Neural Information Processing Systems 35, 478-491, 2022
292022
Quantitative propagation of chaos for SGD in wide neural networks
V De Bortoli, A Durmus, X Fontaine, U Simsekli
Advances in Neural Information Processing Systems 33, 278-288, 2020
292020
Review of wavelet‐based unsupervised texture segmentation, advantage of adaptive wavelets
Y Huang, V De Bortoli, F Zhou, J Gilles
IET Image Processing 12 (9), 1626-1638, 2018
272018
Convergence rates and approximation results for SGD and its continuous-time counterpart
X Fontaine, V De Bortoli, A Durmus
Conference on Learning Theory, 1965-2058, 2021
25*2021
Diffusion Schr÷dinger bridge matching
Y Shi, V De Bortoli, A Campbell, A Doucet
Advances in Neural Information Processing Systems 36, 2024
242024
Maximum likelihood estimation of regularization parameters in high-dimensional inverse problems: An empirical Bayesian approach. part ii: Theoretical analysis
V De Bortoli, A Durmus, M Pereyra, AF Vidal
SIAM Journal on Imaging Sciences 13 (4), 1990-2028, 2020
242020
Can Push-forward Generative Models Fit Multimodal Distributions?
A Salmona, V De Bortoli, J Delon, A Desolneux
Advances in Neural Information Processing Systems 35, 10766-10779, 2022
222022
Convergence of diffusions and their discretizations: from continuous to discrete processes and back
V De Bortoli, A Durmus
arXiv preprint arXiv:1904.09808, 2019
212019
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Articles 1–20