6 papers
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…
In situ Learning-Based Spin Engineering of Pulsed Dynamic Nuclear Polarization
Filip V. Jensen, José P. Carvalho, Nino Wili +6
Pulsed Dynamic Nuclear Polarization (DNP) is currently receiving substantial interest as a means to enhance the sensitivity of nuclear magnetic resonance (NMR) and magnetic resonan…
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…
Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising Diffusions
Sören Christensen, Jan Kallsen, Claudia Strauch +1
We introduce a new class of generative diffusion models that, unlike conventional denoising diffusion models, achieve a time-homogeneous structure for both the noising and denoisin…
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…
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 disjo…