Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare
in a geometrically faithful way point clouds and more generally probability distributions. The …

Abstract Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a different but …

In computer vision, one is often confronted with problems of domain shifts, which occur when
one applies a classifier trained on a source dataset to target data sharing similar …

The ability to compare two degenerate probability distributions, that is two distributions
supported on low-dimensional manifolds in much higher-dimensional spaces, is a crucial …

Abstract Multi-Source Domain Adaptation (MSDA) techniques have attracted widespread
attention due to their availability to transfer knowledge from multiple source domains to the …

Transport-based techniques for signal and data analysis have recently received increased
interest. Given their ability to provide accurate generative models for signal intensities and …

Optimal transport (OT) and maximum mean discrepancies (MMD) are now routinely used in
machine learning to compare probability measures. We focus in this paper on Sinkhorn …

Domain adaptation is one of the most challenging tasks of modern data analytics. If the
adaptation is done correctly, models built on a specific data representation become more …

This paper deals with the unsupervised domain adaptation problem, where one wants to
estimate a prediction function $ f $ in a given target domain without any labeled sample by …