Although deep networks are typically used to approximate functions over high dimensional
inputs, recent work has increased interest in neural networks as function approximators for …

Well known to the machine learning community, the random feature model is a parametric
approximation to kernel interpolation or regression methods. It is typically used to …

We propose a novel framework for learning stabilizable nonlinear dynamical systems for
continuous control tasks in robotics. The key contribution is a control-theoretic regularizer for …

Supervised operator learning centers on the use of training data, in the form of input-output
pairs, to estimate maps between infinite-dimensional spaces. It is emerging as a powerful …

This paper provides a comprehensive error analysis of learning with vector-valued random
features (RF). The theory is developed for RF ridge regression in a fully general infinite …

Motivated by objects such as electric fields or fluid streams, we study the problem of learning
stochastic fields, ie stochastic processes whose samples are fields like those occurring in …

The problem of efficient approximation of a linear operator induced by the Gaussian or
softmax kernel is often addressed using random features (RFs) which yield an unbiased …

A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the
system can be expressed in the linear span of a set of known basis functions. While this …

Randomized algorithms exploit stochasticity to reduce computational complexity. One
important example is random feature regression (RFR) that accelerates Gaussian process …

We propose a new non-parametric framework for learning incrementally stable dynamical
systems x'= f (x) from a set of sampled trajectories. We construct a rich family of smooth …