1 citations · 1 across the 9 of their papers we have counts for
10 papers
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…
Variational Learning for Insertion-based Generation
Yangtian Zhang, Zhe Wang, Arthur Gretton +4
Non-monotonic sequence generation methods, such as masked diffusion models, provide a flexible alternative to left-to-right autoregressive modeling by allowing tokens to be generat…
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…
Sobolev Regularized MMD Gradient Flow
Chenyang Tian, Bharath K. Sriperumbudur, Arthur Gretton +1
We propose Sobolev-regularized Maximum Mean Discrepancy (SrMMD) gradient flow, a regularized variant of maximum mean discrepancy (MMD) gradient flow based on a gradient penalty on…
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…
Fast Best-in-Class Regret for Contextual Bandits
Samuel Girard, Aurelien Bibaut, Arthur Gretton +2
We study the problem of stochastic contextual bandits in the agnostic setting, where the goal is to compete with the best policy in a given class without assuming realizability or…
Closed-Form Last Layer Optimization
Alexandre Galashov, Nathaël Da Costa, Liyuan Xu +2
Neural networks are typically optimized with variants of stochastic gradient descent. Under a squared loss, however, the optimal solution to the linear last layer weights is known…
Learn to Guide Your Diffusion Model
Alexandre Galashov, Ashwini Pokle, Arnaud Doucet +3
Classifier-free guidance (CFG) is a widely used technique for improving the perceptual quality of samples from conditional diffusion models. It operates by linearly combining condi…
Distributional Diffusion Models with Scoring Rules
Valentin De Bortoli, Alexandre Galashov, J. Swaroop Guntupalli +4
Diffusion models generate high-quality synthetic data. They operate by defining a continuous-time forward process which gradually adds Gaussian noise to data until fully corrupted.…
Accelerated Diffusion Models via Speculative Sampling
Valentin De Bortoli, Alexandre Galashov, Arthur Gretton +1
Speculative sampling is a popular technique for accelerating inference in Large Language Models by generating candidate tokens using a fast draft model and accepting or rejecting t…