A key learning scenario in large-scale applications is that of federated learning, where a
centralized model is trained based on data originating from a large number of clients. We …

Graph neural networks (GNNs) which apply the deep neural networks to graph data have
achieved significant performance for the task of semi-supervised node classification …

In this work we formulate and formally characterize group fairness as a multi-objective
optimization problem, where each sensitive group risk is a separate objective. We propose a …

Min–max problems have broad applications in machine learning, including learning with
non-decomposable loss and learning with robustness to data distribution. Convex–concave …

We show that many machine learning goals can be expressed as “rate constraints” on a
model's predictions. We study the problem of training non-convex models subject to these …

Nonconvex minimax problems appear frequently in emerging machine learning
applications, such as generative adversarial networks and adversarial learning. Simple …

We consider a recently introduced framework in which fairness is measured by worst-case
outcomes across groups, rather than by the more standard differences between group …

We study the global convergence of policy optimization for finding the Nash equilibria (NE)
in zero-sum linear quadratic (LQ) games. To this end, we first investigate the landscape of …

In this paper, we consider the problem of Gaussian process (GP) optimization with an added
robustness requirement: The returned point may be perturbed by an adversary, and we …

We present pairwise fairness metrics for ranking models and regression models that form
analogues of statistical fairness notions such as equal opportunity, equal accuracy, and …