Valentin De Bortoli
I do research at Google DeepMind in London. I work on diffusion models — how to build them, how to sample from them efficiently, and why they work — and more broadly on sampling, optimal transport and Markov chain theory.
Short CV
- 2023 – now · Research Scientist, Google DeepMind
- 2022 – now · CNRS Researcher (on leave), ENS Ulm
- 2020 – 2022 · Postdoctoral Researcher, University of Oxford
- 2017 – 2020 · PhD, ENS Paris-Saclay
news
| Aug 01, 2026 | The DiffusionGemma technical report is out. |
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| Sep 20, 2025 | Three papers accepted at NeurIPS 2025, on inference-time annealing, memorization in diffusion models, and proximal MCMC for non-log-concave sampling. |
| May 01, 2025 | Distributional Diffusion Models with Scoring Rules accepted at ICML 2025. |
selected publications
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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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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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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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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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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.