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 …

Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

Optimal transport has recently been reintroduced to the machine learning community thanks
in part to novel efficient optimization procedures allowing for medium to large scale …

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

Unsupervised domain adaptation (UDA) enables the transfer of models trained on source
domains to unlabeled target domains. However, transferring complex time series models …

Deep neural networks suffer from performance decay when there is domain shift between
the labeled source domain and unlabeled target domain, which motivates the research on …

Plugin estimation of smooth optimal transport maps Page 1 The Annals of Statistics 2024, Vol.
52, No. 3, 966–998 https://doi.org/10.1214/24-AOS2379 © Institute of Mathematical Statistics …

Adversarial learning has demonstrated good performance in the unsupervised domain
adaptation setting, by learning domain-invariant representations. However, recent work has …

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled
source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge …