Optimal Transport (OT) theory has seen an increasing amount of attention from the computer
science community due to its potency and relevance in modeling and machine learning. It …

We introduce a new class of Frank-Wolfe algorithms for minimizing differentiable functionals
over probability measures. This framework can be shown to encompass a diverse range of …

In this note, we provide upper bounds on the expectation of the supremum of empirical
processes indexed by Hölder classes of any smoothness and for any distribution supported …

This paper studies the equitable and optimal transport (EOT) problem, which has many
applications such as fair division problems and optimal transport with multiple agents etc. In …

Optimal transport is a machine learning problem with applications including distribution
comparison, feature selection, and generative adversarial networks. In this paper, we …

We consider the problem of allocating a distribution of items to $ n $ recipients where each
recipient has to be allocated a fixed, pre-specified fraction of all items, while ensuring that …

This paper considers the decentralized (discrete) optimal transport (D-OT) problem. In this
setting, a network of agents seeks to design a transportation plan jointly, where the cost …

This thesis investigates the problem of classification in presence of adversarial attacks.
Anadversarial attack is a small and humanly imperceptible perturbation of input designed …

In this paper, we propose a novel numerical scheme to optimize the gradient flows for
learning energy-based models (EBMs). From a perspective of physical simulation, we …

Machine learning algorithms are celebrated for their impressive performance on many
tasksthat we thought were dedicated to human minds, from handwritten digits recognition …