In mechanical system dynamics, an eigenvalue with zero frequency signifies a rigid body
motion mode. You are in this situation when you consider a system but do not apply the …

Many problems of recent interest in statistics and machine learning can be posed in the
framework of convex optimization. Due to the explosion in size and complexity of modern …

We consider two federated learning algorithms for training partially personalized models,
where the shared and personal parameters are updated either simultaneously or alternately …

Influence functions efficiently estimate the effect of removing a single training data point on a
model's learned parameters. While influence estimates align well with leave-one-out …

Despite advances in scalable models, the inference tools used for Gaussian processes
(GPs) have yet to fully capitalize on developments in computing hardware. We present an …

The C++ language is often used for implementing functionality that is performance and/or
resource sensitive. While the standard C++ library provides many useful algorithms (such as …

Normalization is known to help the optimization of deep neural networks. Curiously, different
architectures require specialized normalization methods. In this paper, we study what …

This open access book provides a comprehensive treatment of recent developments in
kernel-based identification that are of interest to anyone engaged in learning dynamic …

In addition to decentralized controllers, the information flow among vehicles can significantly
affect the dynamics of a platoon. This paper studies the influence of information flow …

The termsurrogate modeling'in computational science and engineering refers to the
development of computationally efficient approximations for expensive simulations, such as …