5 citations · 7 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
Adversarial Learning of Classifier-Free Guidance Schedules
Ashwini Pokle, Alexandre Galashov, Arnaud Doucet +2
Modern text-to-image diffusion models rely on classifier-free guidance (CFG) to achieve high image fidelity and text alignment. However, CFG typically applies a static, global scal…
DiffusionGemma Technical Report
DiffusionGemma Team, Adrien Ali Taïga, James Assiene +41
We introduce DiffusionGemma, an experimental open-weight language model that uses discrete diffusion to generate text at exceptionally high speed. Rather than decoding one token at…
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…
Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
Bariscan Bozkurt, Alexandre Galashov, Dimitri Meunier +3
We propose a novel doubly robust neural mean-embedding framework for proxy causal learning (PCL) for continuous and structured treatments. PCL uses proxy variables and bridge funct…