5 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
Data-driven optimal stopping: A pure exploration analysis
Sören Christensen, Niklas Dexheimer, Claudia Strauch
The standard theory of optimal stopping is based on the idealised assumption that the underlying process is essentially known. In this paper, we drop this restriction and study dat…
Data-driven rules for multidimensional reflection problems
Sören Christensen, Asbjørn Holk Thomsen, Lukas Trottner
Over the recent past data-driven algorithms for solving stochastic optimal control problems in face of model uncertainty have become an increasingly active area of research. Howeve…
Is Learning in Biological Neural Networks based on Stochastic Gradient Descent? An analysis using stochastic processes
Sören Christensen, Jan Kallsen
In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued th…
Learning to reflect: A unifying approach for data-driven stochastic control strategies
Sören Christensen, Claudia Strauch, Lukas Trottner
Stochastic optimal control problems have a long tradition in applied probability, with the questions addressed being of high relevance in a multitude of fields. Even though theoret…