3 accepted papers
Optimal Transport (OT) has become a popular tool in machine learning to align finite datasets typically lying in the same vector space. To expand the range of possible applications, Co-Optimal Transport (Co-OT) jointly estimates two distinct transport plans, one for the rows (points) and one for the…
The Optimal transport (OT) problem and its associated Wasserstein distance have recently become a topic of great interest in the machine learning community. However, the underlying optimization problem is known to have two major restrictions: (i) it largely depends on the choice of the cost function…
Domain Adaptation aims at benefiting from a labeled dataset drawn from a source distribution to learn a model from examples generated from a different but related target distribution. Creating a domain-invariant representation between the two source and target domains is the most widel…