48 citations · 101 across the 11 of their papers we have counts for
14 papers
Gumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps
Yeongmin Kim, Arnaud Doucet, Andrew Campbell +3
We present Gumbel Straight Flow (GSF), a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive language (AR) model. We theoretica…
Improved Distributional Diffusion Models
Tommaso Martorella, Alexandre Galashov, Felix Krause +4
Distributional Diffusion Models (DDMs) replace the standard mean-prediction denoiser with a \emph{distributional} denoiser trained via a scoring rule objective, learning a stochast…
Simplex Diffusion Models
Justin Deschenaux, Alexandre Galashov, Andrew Campbell +4
Diffusion models have revolutionized generative modeling for continuous data through the gradual refinement of a belief state. This iterative refinement has not yet carried over to…
On the Wasserstein Gradient Flow Interpretation of Drifting Models
Arthur Gretton, Li Kevin Wenliang, Alexandre Galashov +3
Recently, Deng et al. (2026) proposed Generative Modeling via Drifting (GMD), a novel framework for generative tasks. This note presents an analysis of GMD through the lens of Wass…
Deep MMD Gradient Flow without adversarial training
Alexandre Galashov, Valentin de Bortoli, Arthur Gretton
We propose a gradient flow procedure for generative modeling by transporting particles from an initial source distribution to a target distribution, where the gradient field on the…
Dynamical Regimes of Diffusion Models
Giulio Biroli, Tony Bonnaire, Valentin de Bortoli +1
Using statistical physics methods, we study generative diffusion models in the regime where the dimension of space and the number of data are large, and the score function has been…
Target Score Matching
Valentin De Bortoli, Michael Hutchinson, Peter Wirnsberger +1
Denoising Score Matching estimates the score of a noised version of a target distribution by minimizing a regression loss and is widely used to train the popular class of Denoising…
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Pierre Marion, Anna Korba, Peter Bartlett +6
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions. Doing so allows us to modify the outcome distribution of sampling pr…
Augmented Bridge Matching
Valentin De Bortoli, Guan-Horng Liu, Tianrong Chen +2
Flow and bridge matching are a novel class of processes which encompass diffusion models. One of the main aspect of their increased flexibility is that these models can interpolate…
Wavelet Score-Based Generative Modeling
Florentin Guth, Simon Coste, Valentin De Bortoli +1
Score-based generative models (SGMs) synthesize new data samples from Gaussian white noise by running a time-reversed Stochastic Differential Equation (SDE) whose drift coefficient…
Convergence of denoising diffusion models under the manifold hypothesis
Valentin De Bortoli
Denoising diffusion models are a recent class of generative models exhibiting state-of-the-art performance in image and audio synthesis. Such models approximate the time-reversal o…
Can Push-forward Generative Models Fit Multimodal Distributions?
Antoine Salmona, Valentin de Bortoli, Julie Delon +1
Many generative models synthesize data by transforming a standard Gaussian random variable using a deterministic neural network. Among these models are the Variational Autoencoders…
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent
Rémi Laumont, Valentin de Bortoli, Andrés Almansa +3
Bayesian methods to solve imaging inverse problems usually combine an explicit data likelihood function with a prior distribution that explicitly models expected properties of the…
On quantitative Laplace-type convergence results for some exponential probability measures, with two applications
Valentin De Bortoli, Agnès Desolneux
Laplace-type results characterize the limit of sequence of measures with density w.r.t the Lebesgue measure $(\mathrm{d} π_\varepsilon / \mathrm{…