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Found 5 papers, 1 papers with code
Multiparameter regularization and aggregation in the context of polynomial functional regression
Most of the recent results in polynomial functional regression have been focused on an in-depth exploration of single-parameter regularization schemes.
On regularized Radon-Nikodym differentiation
We discuss the problem of estimating Radon-Nikodym derivatives.
Adaptive learning of density ratios in RKHS
Estimating the ratio of two probability densities from finitely many observations of the densities is a central problem in machine learning and statistics with applications in two-sample testing, divergence estimation, generative modeling, covariate shift adaptation, conditional density estimation, and novelty detection.
General regularization in covariate shift adaptation
Sample reweighting is one of the most widely used methods for correcting the error of least squares learning algorithms in reproducing kernel Hilbert spaces (RKHS), that is caused by future data distributions that are different from the training data distribution.
Domain Generalization by Functional Regression
The problem of domain generalization is to learn, given data from different source distributions, a model that can be expected to generalize well on new target distributions which are only seen through unlabeled samples.
