Valentin De Bortoli

Publications

Note that this list can be also seen and filtered here.

. Spectral Diffusion Processes. 2022 - Submitted (conference).

. Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows. 2022 - Submitted (journal).

. Convergence of denoising diffusion models under the manifold hypothesis. 2022 - Accepted at TMLR (journal).

. Riemannian Diffusion Schrödinger Bridges. 2022 - Accepted at ICML (conference).

. Conditional Simulation Using Diffusion Schrödinger Bridges. 2022 - Accepted at UAI (conference).

. A Continuous Time Framework for Discrete Denoising Models. 2022 - Accepted at Neurips (conference).

. Can Push-forward Generative Models Fit Multimodal Distributions. 2022 - Accepted at Neurips (conference).

. Wavelet Score-Based Generative Modeling. 2022 - Accepted at Neurips (conference).

. Riemannian Score Based Generative Modeling. 2022 - Accepted at Neurips (conference).

. Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent . 2022 - Accepted at JMIV (journal).

. On quantitative Laplace-type convergence results for some exponential probability measures, with two applications . 2021 - Accepted at JMLR (journal).

. Simulating Diffusion Bridges with Score Matching . 2021 - Submitted (journal)

. Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure . 2021 - Submitted (journal)

. Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling. 2021 - Accepted at NeuRIPS (conference).

. Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie. 2021 - Accepted at SIAM Imaging Science(journal).

. Maximum entropy methods for texture synthesis: theory and practice. 2021 - Published in SIMODS (journal).

. Quantitative Propagation of Chaos for SGD in Wide Neural Networks. 2020 - Accepted at NeuRIPS (conference).

. Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian. Part I: Methodology and Experiments approach. 2020 - Published in SIIMS (journal).

. Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian. Part II: Theoretical Analysis approach. 2020 - Published in SIIMS (journal).

. Approximate Bayesian Computation with the Sliced-Wasserstein Distance. 2020 - Accepted at ICASSP (conference) (Best student paper award).

. Redundancy in Gaussian random fields. 2020 - Published in ESAIM: Probability and Statistics

. Continuous and Discrete-Time Analysis of Stochastic Gradient Descent for Convex and Non-Convex Functions. 2021 - Accepted at COLT (conference).

. Patch redundancy in images: a statistical testing framework and some applications. 2019 - Published in SIAM Journal on Imaging Science (journal).

. Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation. 2019 - Accepted at Statistics and Computing (journal).

. Convergence of diffusion and their discretizations: from continuous to discrete processes and back. 2019 - Submitted (journal).

. Macrocanonical models for texture synthesis. 2019 - Accepted at Scale Space and Variational Methods in Computer Vision (conference).

. Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets. 2018 - Published in IET (journal).

Teaching

Generative Modeling

MVA course (M2) generative modeling

Differential Calculus

ENS Paris Saclay L3 differential calculus

Hilbert Analysis

ENS Paris Saclay agrégation Hilbert analysis

Fourier Analysis

ENS Paris Saclay agrégation Fourier analysis

Optimization

ENS Paris Saclay M1 optimization

Contact