We study the complexity of approximating the multimarginal optimal transport (MOT)
distance, a generalization of the classical optimal transport distance, considered here …

Abstract Multimarginal Optimal Transport (MOT) has attracted significant interest due to
applications in machine learning, statistics, and the sciences. However, in most applications …

We study a family of adversarial multiclass classification problems and provide equivalent
reformulations in terms of: 1) a family of generalized barycenter problems introduced in the …

Abstract Multimarginal Optimal Transport (MOT) is the problem of linear programming over
joint probability distributions with fixed marginals. A key issue in many applications is the …

Alternating minimization (AM) procedures are practically efficient in many applications for
solving convex and non-convex optimization problems. On the other hand, Nesterov's …

First-order methods for solving convex optimization problems have been at the forefront of
mathematical optimization in the last 20 years. The rapid development of this important class …

Multi-marginal optimal transport enables one to compare multiple probability measures,
which increasingly finds application in multi-task learning problems. One practical limitation …

We study the problem of the decentralized computation of entropy-regularized semi-discrete
Wasserstein barycenters over a network. Building upon recent primal-dual approaches, we …

We propose a numerical algorithm for computing approximately optimal solutions of the
matching for teams problem. Our algorithm is efficient for problems involving a large number …

In this work we propose a batch multimarginal version of the Greenkhorn algorithm for the
entropic-regularized optimal transport problem. This framework is general enough to cover …