45 citations · 85 across the 7 of their papers we have counts for
8 papers
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…
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
Mark Rowland, Li Kevin Wenliang, Rémi Munos +3
We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative…
Score-based generative models learn manifold-like structures with constrained mixing
Li Kevin Wenliang, Ben Moran
How do score-based generative models (SBMs) learn the data distribution supported on a low-dimensional manifold? We investigate the score model of a trained SBM through its linear…
TacticAI: an AI assistant for football tactics
Zhe Wang, Petar Veličković, Daniel Hennes +20
Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains…
Memory-Based Meta-Learning on Non-Stationary Distributions
Tim Genewein, Grégoire Delétang, Anian Ruoss +7
Memory-based meta-learning is a technique for approximating Bayes-optimal predictors. Under fairly general conditions, minimizing sequential prediction error, measured by the log l…
Neural Networks and the Chomsky Hierarchy
Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya +8
Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in t…
COT-GAN: Generating Sequential Data via Causal Optimal Transport
Tianlin Xu, Li K. Wenliang, Michael Munn +1
We introduce COT-GAN, an adversarial algorithm to train implicit generative models optimized for producing sequential data. The loss function of this algorithm is formulated using…