However, these methods typically rely on strict assumptions and are limited to certain types of label noise. In this paper, we reformulate the label-noise problem from a generative-model perspective, i.e., labels are generated by gradually refining an initial random guess.

As such, it is often desired to select a small number of informative features to consider in subsequent analyses.

This paper presents new quadrature rules for functions in a reproducing kernel Hilbert space using nodes drawn by a sampling algorithm known as randomly pivoted Cholesky. The resulting computational procedure compares favorably to previous kernel quadrature methods, which either achieve low accuracy or require solving a computationally challenging sampling problem.

Large-scale pre-trained generative models are taking the world by storm, due to their abilities in generating creative content.

In this work, we propose data-efficient and interpretable machine learning models for learning to solve numeric planning tasks.