4 papers
Dimension-free Bounds for Covariance Estimation with Tensor-Train Structure
Artsiom Patarusau, Nikita Puchkin, Maxim Rakhuba +1
We consider a problem of covariance estimation from a sample of i.i.d. high-dimensional random vectors. To avoid the curse of dimensionality, we impose an additional assumption on…
Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
Fedor Noskov, Nikita Puchkin, Vladimir Spokoiny
We consider a problem of high-dimensional linear regression with random design. We suggest a novel approach referred to as error-in-operator which does not estimate the design cova…
Sharper dimension-free bounds on the Frobenius distance between sample covariance and its expectation
Nikita Puchkin, Fedor Noskov, Vladimir Spokoiny
We study properties of a sample covariance estimate given a finite sample of i.i.d. centered random elements in with the covariance matrix . We derive…
Reconstruction of manifold embeddings into Euclidean spaces via intrinsic distances
Nikita Puchkin, Vladimir Spokoiny, Eugene Stepanov +1
We consider the problem of reconstructing an embedding of a compact connected Riemannian manifold in a Euclidean space up to an almost isometry, given the information on intrinsic…