3 citations · 3 across the 5 of their papers we have counts for
5 papers
A Minimax Theory of Nonparametric Regression Under Covariate Shift
Petr Zamolodtchikov
We consider nonparametric regression under covariate shift, where we observe samples from both the target distribution and a related but distinct source distribution. We introduce…
On empirical Hodge Laplacians under the manifold hypothesis
Jan-Paul Lerch, Martin Wahl, Petr Zamolodtchikov
Given i.i.d. observations uniformly distributed on a closed submanifold of the Euclidean space, we study higher-order generalizations of graph Laplacians, so-called Hodge Laplacian…
Generative Modelling via Quantile Regression
Johannes Schmidt-Hieber, Petr Zamolodtchikov
We link conditional generative modelling to quantile regression. We propose a suitable loss function and derive minimax convergence rates for the associated risk under smoothness a…
Transfer Learning under Covariate Shift: Local -Nearest Neighbours Regression with Heavy-Tailed Design
Petr Zamolodtchikov, Hanyuan Hang
Covariate shift is a common transfer learning scenario where the marginal distributions of input variables vary between source and target data while the conditional distribution of…
Local convergence rates of the nonparametric least squares estimator with applications to transfer learning
Johannes Schmidt-Hieber, Petr Zamolodtchikov
Convergence properties of empirical risk minimizers can be conveniently expressed in terms of the associated population risk. To derive bounds for the performance of the estimator…