This article reviews the gradient sampling methodology for solving nonsmooth, nonconvex
optimization problems. We state an intuitively straightforward gradient sampling algorithm …

We study fair machine learning (ML) under predictive uncertainty to enable reliable and
trustworthy decision-making. The seminal work of'equalized coverage'proposed an …

Dynamical systems are at the core of computational models for a wide range of complex
phenomena and, as a consequence, the simulation of dynamical systems has become a …

The applications of direct policy search in reinforcement learning and continuous control
have received increasing attention. In this work, we present novel theoretical results on the …

Direct policy search has been widely applied in modern reinforcement learning and
continuous control. However, the theoretical properties of direct policy search on nonsmooth …

This paper concerns dictionary learning, ie, sparse coding, a fundamental representation
learning problem. We show that a subgradient descent algorithm, with random initialization …

An optimization algorithm for nonsmooth nonconvex constrained optimization problems with
upper-objective functions is proposed and analyzed. Upper-is a weakly concave property …

Autonomous vehicles (AVs) must share the driving space with other drivers and often
employ conservative motion planning strategies to ensure safety. These conservative …

Integrating artificial intelligence (AI) and robotics in the aerospace and defense industry has
revolutionized how things are done. This has led to cost savings, faster design and …

The identification of reduced-order models from high-dimensional data is a challenging task,
and even more so if the identified system should not only be suitable for a certain data set …