2 citations · 3 across the 3 of their papers we have counts for
4 papers
Convergence bounds for nonlinear least squares and applications to tensor recovery
Philipp Trunschke
We consider the problem of approximating a function in general nonlinear subsets of when only a weighted Monte Carlo estimate of the -norm can be computed. Of particular…
A block-sparse Tensor Train Format for sample-efficient high-dimensional Polynomial Regression
Michael Götte, Reinhold Schneider, Philipp Trunschke
Low-rank tensors are an established framework for high-dimensional least-squares problems. We propose to extend this framework by including the concept of block-sparsity. In the co…
Pricing high-dimensional Bermudan options with hierarchical tensor formats
Christian Bayer, Martin Eigel, Leon Sallandt +1
An efficient compression technique based on hierarchical tensors for popular option pricing methods is presented. It is shown that the "curse of dimensionality" can be alleviated f…
The Oracle of DLphi
Dominik Alfke, Weston Baines, Jan Blechschmidt +24
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of l…