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

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

Different distribution shifts require different algorithmic and operational interventions.
Methodological research must be grounded by the specific shifts they address. Although …

We consider the problem of optimal trajectory tracking for unknown systems. A novel data-
enabled predictive control (DeePC) algorithm is presented that computes optimal and safe …

Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …

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 …

We show that several machine learning estimators, including square-root least absolute
shrinkage and selection and regularized logistic regression, can be represented as …

In this paper, we survey the primary research on the theory and applications of
distributionally robust optimization (DRO). We start with reviewing the modeling power and …

Federated learning is a distributed paradigm that aims at training models using samples
distributed across multiple users in a network while keeping the samples on users' devices …

Reinforcement learning (RL) has demonstrated remarkable achievements in simulated
environments. However, carrying this success to real environments requires the important …