publications
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2026
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DiffusionGemma Technical Report
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Diffusion Fine-tuning with Rewarded Moment Matching Distillation
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Adversarial Learning of Classifier-Free Guidance Schedules
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Accelerating Speculative Diffusions via Block Verification
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On the Wasserstein Gradient Flow Interpretation of Drifting Models
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AsyncPatch Diffusion: Spatially-Flexible Image Generation
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Seasoning Generative Models for a Generalization Aftertaste
2025
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Dimension-Free Error Estimate for Diffusion Model and Optimal Scheduling
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Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities
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On the Edge of Memorization in Diffusion Models
Studies where diffusion models stop generalising and start reproducing their training data, and what controls the boundary between the two.
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Distributional Diffusion Models with Scoring Rules
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From Stability of Langevin Diffusion to Convergence of Proximal MCMC for Non-Log-Concave Sampling
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Self-Speculative Masked Diffusions
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Learn to Guide Your Diffusion Model
2024
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Particle Denoising Diffusion Sampler
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Schrödinger Bridge Flow for Unpaired Data Translation
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Implicit Diffusion: Efficient Optimization through Stochastic Sampling
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Deep MMD Gradient Flow without Adversarial Training
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Solving a Class of Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows
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Target Score Matching
2023
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Diffusion Schrödinger Bridge Matching
Reformulates the Schrodinger bridge as iterative Markovian fitting, removing the error that accumulated across outer iterations in earlier bridge solvers.
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SE(3) Diffusion Model with Application to Protein Backbone Generation
A diffusion model on SE(3) that generates protein backbones by denoising the rigid-body frame of each residue.
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Trans-Dimensional Generative Modeling via Jump Diffusion Models
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Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization
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Particle Guidance: Non-I.I.D. Diverse Sampling with Diffusion Models
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Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters
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Metropolis Sampling for Constrained Diffusion Models
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Geometric Neural Diffusion Processes
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Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models
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Diffusion Models for Constrained Domains
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Augmented Bridge Matching
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Diffusion Schrödinger Bridges for Bayesian Computation
2022
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Convergence of Denoising Diffusion Models under the Manifold Hypothesis
Convergence guarantees for denoising diffusions when the data lies on a low-dimensional manifold - the regime where the score is unbounded and earlier analyses break down.
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From Denoising Diffusions to Denoising Markov Models
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Riemannian Score-Based Generative Modelling
Extends score-based generative models from Euclidean space to Riemannian manifolds, so data living on spheres, tori and other geometries is modelled in its own coordinates.
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A Continuous Time Framework for Discrete Denoising Models
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Wavelet Score-Based Generative Modeling
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Can Push-Forward Generative Models Fit Multimodal Distributions?
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Unbiased Constrained Sampling with Self-Concordant Barrier Hamiltonian Monte Carlo
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Conditional Simulation Using Diffusion Schrödinger Bridges
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Riemannian Diffusion Schrödinger Bridge
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Spectral Diffusion Processes
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On Maximum a Posteriori Estimation with Plug & Play Priors and Stochastic Gradient Descent
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An Introduction to Bayesian Imaging with Data-Driven Priors Encoded by Neural Networks
2021
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Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Casts generative modelling as a Schrodinger bridge - an entropic optimal transport problem - and solves it by iterative proportional fitting on learned scores.
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On Quantitative Laplace-Type Convergence Results for Some Exponential Probability Measures, with Two Applications
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Simulating Diffusion Bridges with Score Matching
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Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure
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Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie
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Maximum Entropy Methods for Texture Synthesis: Theory and Practice
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Continuous and Discrete-Time Analysis of Stochastic Gradient Descent for Convex and Non-Convex Functions
2020
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Quantitative Propagation of Chaos for SGD in Wide Neural Networks
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Approximate Bayesian Computation with the Sliced-Wasserstein Distance
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Maximum Likelihood Estimation of Regularisation Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part I: Methodology and Experiments
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Maximum Likelihood Estimation of Regularisation Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis
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Redundancy in Gaussian Random Fields
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Efficient Stochastic Optimisation by Unadjusted Langevin Monte Carlo. Application to Maximum Marginal Likelihood and Empirical Bayesian Estimation
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Non-Local Statistics in Images: Modelling, Estimation and Sampling
2019
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Patch Redundancy in Images: A Statistical Testing Framework and Some Applications
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Macrocanonical Models for Texture Synthesis
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Convergence of Diffusions and Their Discretizations: From Continuous to Discrete Processes and Back
2018
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Review of Wavelet-Based Unsupervised Texture Segmentation, Advantage of Adaptive Wavelets