Any explanation that faithfully explains this type of model needs to be in agreement with this invariance property.

The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model class one optimizes over--and the more data one has access to--the more one can improve performance. As models get deployed in a variety of real-world scenarios, they inevitably face strategic environments.

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.

We give a sufficient criterion to ensure that the problem is well-conditioned.

However, the heterogeneity of protein data constitutes a problem for reproducible machine learning.

We study the problem of online adaptive policy selection for nonlinear time-varying discrete-time dynamical systems.

Concretely, we prove that polynomial-sized message-passing GNN's can learn the