The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …

In this article we study the problem of finite-time constrained optimal control of unknown
stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive …

Two stochastic model predictive control algorithms, which are referred to as distributionally
robust model predictive control algorithms, are proposed in this article for a class of discrete …

The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …

This paper provides an overview of developments in robust optimization since 2007. It seeks
to give a representative picture of the research topics most explored in recent years …

Abstract Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is
studied in various fields such as engineering, computer science, economics, and …

Standard stochastic control methods assume that the probability distribution of uncertain
variables is available. Unfortunately, in practice, obtaining accurate distribution information …

An output feedback stochastic model predictive control is proposed in this article for a class
of stochastic linear discrete-time systems, in which the uncertainties from external …

The objective of uncertainty quantification is to certify that a given physical, engineering or
economic system satisfies multiple safety conditions with high probability. A more ambitious …