Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth
century and has led to a plethora of methods for answering many theoretical and applied …

Given two point sets A and B in ℝ d of size n each, for some constant dimension d≥ 1, and a
parameter ε> 0, we present a deterministic algorithm that computes, in n·(ε− 1 log n) O (d) …

In this work, we present a mathematical formulation for machine learning of (1) functions on
symmetric matrices that are invariant with respect to the action of permutations by …

Optimal Transport (OT) provides a useful geometric framework to estimate the permutation
matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the …

Metrics such as diversity and novelty have become important, beside accuracy, in the design
of Recommender Systems (RSs), in response the increasing users' heterogeneity …

Maximum cardinality bipartite matching is an important graph optimization problem with
several applications. For instance, maximum cardinality matching in a $\delta $-disc graph …

Intractable generative models, or simulators, are models for which the likelihood is
unavailable but sampling is possible. Most approaches to parameter inference in this setting …

Many real-world problems can be formulated as the alignment between two geometric
patterns. Previously, a great amount of research focus on the alignment of two-dimensional …

Obtaining solutions to optimal transportation (OT) problems is typically intractable when
marginal spaces are continuous. Recent research has focused on approximating continuous …

In machine learning and deep learning paradigms, high-dimensional vectorized
embeddings have emerged as a powerful and useful method for representing structured …