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math.ST2026

Statistical Convergence of Spherical First Hitting Diffusion Models

Simon Bienewald, Lukas Trottner

Denoising diffusion models have evolved into a state-of-the-art method for tasks in various fields, such as denoising and generation of images, text generation, or generation of sy…

math.ST2026

Reflected diffusion models adapt to low-dimensional data

Asbjørn Holk, Claudia Strauch, Lukas Trottner

While the mathematical foundations of score-based generative models are increasingly well understood for unconstrained Euclidean spaces, many practical applications involve data re…

math.ST2026

Statistical guarantees for denoising reflected diffusion models

Asbjørn Holk, Claudia Strauch, Lukas Trottner

In recent years, denoising diffusion models have become a crucial area of research due to their abundance in the rapidly expanding field of generative AI. While recent statistical…

math.ST2025

Model-free filtering in high dimensions via projection and score-based diffusions

Sören Christensen, Jan Kallsen, Claudia Strauch +1

We consider the problem of recovering a latent signal from its noisy observation . The unknown law of , and in particular its support , are ac…

math.ST2025

Multivariate change estimation for a stochastic heat equation from local measurements

Anton Tiepner, Lukas Trottner

We study a stochastic heat equation with piecewise constant diffusivity having a jump at a hypersurface that splits the underlying space , into two dis…

math.ST2024

Change point estimation for a stochastic heat equation

Markus Reiß, Claudia Strauch, Lukas Trottner

We study a change point model based on a stochastic partial differential equation (SPDE) corresponding to the heat equation governed by the weighted Laplacian $Δ_\vartheta = \nabl…