We provide theoretical analyses for two algorithms that solve the regularized optimal
transport (OT) problem between two discrete probability measures with at most $ n $ atoms …

We generalize the concept of adaptive sampling rules to the sketch-and-project method for
solving linear systems. Analyzing adaptive sampling rules in the sketch-and-project setting …

Optimal transport distances have become a classic tool to compare probability distributions
and have found many applications in machine learning. Yet, despite recent algorithmic …

We present several new complexity results for the entropic regularized algorithms that
approximately solve the optimal transport (OT) problem between two discrete probability …

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between
a discrete and a generic (possibly non-discrete) probability measure, are believed to be …

We introduce in this paper a novel strategy for efficiently approximating the Sinkhorn
distance between two discrete measures. After identifying neglectable components of the …

We consider the classical version of the optimal partial transport problem. Let µ (with a mass
of U) and ν (with a mass of S) be two discrete mass distributions with S≤ U and let n be the …

This paper improves the state-of-the-art rate of a first-order algorithm for solving entropy
regularized optimal transport. The resulting rate for approximating the optimal transport (OT) …

A primal-dual accelerated stochastic gradient descent with variance reduction algorithm
(PDASGD) is proposed to solve linear-constrained optimization problems. PDASGD could …

Significance: We demonstrated the potential of using domain adaptation on functional near-
infrared spectroscopy (fNIRS) data to classify different levels of n-back tasks that involve …