Gromov-Wasserstein distance has found many applications in machine learn...
Optimal transport (OT) is a powerful geometric tool used to compare and ...
We study the use of amortized optimization to predict optimal transport ...
Optimal transport (OT) compares probability distributions by computing a...
Optimal transport (OT) theory underlies many emerging machine learning (...
Machine learning and data mining algorithms have been increasingly used
...
The theoretical analysis of deep neural networks (DNN) is arguably among...
Most of existing deep learning models rely on excessive amounts of label...
In this paper, we introduce and formalize a rank-one partitioning learni...
All famous machine learning algorithms that correspond to both supervise...
In this paper, we propose a new feature selection method for unsupervise...
Many information retrieval algorithms rely on the notion of a good dista...
In this paper, we propose to tackle the problem of reducing discrepancie...
In this paper, we present a novel method for co-clustering, an unsupervi...
The ability of a human being to extrapolate previously gained knowledge ...
Domain adaptation (DA) is an important and emerging field of machine lea...