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Found 5 papers, 3 papers with code
Convergence bounds for local least squares approximation
We consider the problem of approximating a function in a general nonlinear subset of $L^2$, when only a weighted Monte Carlo estimate of the $L^2$-norm can be computed.
Convergence bounds for nonlinear least squares and applications to tensor recovery
We reexamine the results of the previous paper and derive a new bound that is able to utilize the regularity of the sought function.
A block-sparse Tensor Train Format for sample-efficient high-dimensional Polynomial Regression
Low-rank tensors are an established framework for high-dimensional least-squares problems.
Pricing high-dimensional Bermudan options with hierarchical tensor formats
An efficient compression technique based on hierarchical tensors for popular option pricing methods is presented.
The Oracle of DLphi
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results.
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