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stat.ML2025
Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems
Robert Gruhlke, Matei Hanu, Claudia Schillings +1
We introduce a gradient-free framework for Bayesian Optimal Experimental Design (BOED) in sequential settings, aimed at complex systems where gradient information is unavailable. O…
stat.ML2024
Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations
David Sommer, Robert Gruhlke, Max Kirstein +2
Sampling from probability densities is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM). In GM in particular, the use of reverse-t…