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Tensor train algorithms for stochastic PDE problems – Sergey Dolgov, University of Bath
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Classroom Contents
Approximating High Dimensional Functions - Mathematical Foundations Workshop
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- 1 Multilevel weighted least squares polynomial approximation – Sören Wolfers, KAUST
- 2 Tensor train algorithms for stochastic PDE problems – Sergey Dolgov, University of Bath
- 3 Approximation of generalized ridge functions in high dimensions – Sandra Keiper
- 4 Ridge functions, their sums, and sparse additive functions – Jan Vybiral, Czech Technical University
- 5 Optimal sampling in weighted least-squares methods: Application to high-dimensional approximation
- 6 Concentration of tempered posteriors and of their variational approximations – Pierre Alquier
- 7 Recovery of ridge functions in the uniform norm – Sebastian Mayer, Universität Bonn
- 8 Score estimation with infinite-dimensional exponential families – Dougal Sutherland, UCL
- 9 Isotonic regression in general dimensions – Richard Samworth, University of Cambridge
