3 citations · 8 across the 9 of their papers we have counts for
9 papers
Partial Gromov-Wasserstein Metric
Yikun Bai, Rocio Diaz Martin, Abihith Kothapalli +3
The Gromov-Wasserstein (GW) distance has gained increasing interest in the machine learning community in recent years, as it allows for the comparison of measures in different metr…
Stereographic Spherical Sliced Wasserstein Distances
Huy Tran, Yikun Bai, Abihith Kothapalli +4
Comparing spherical probability distributions is of great interest in various fields, including geology, medical domains, computer vision, and deep representation learning. The uti…
LCOT: Linear circular optimal transport
Rocio Diaz Martin, Ivan Medri, Yikun Bai +4
The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning. In this p…
PT: Partial Transport Distances
Xinran Liu, Yikun Bai, Huy Tran +3
Optimal transport and its related problems, including optimal partial transport, have proven to be valuable tools in machine learning for computing meaningful distances between pro…
Linear Optimal Partial Transport Embedding
Yikun Bai, Ivan Medri, Rocio Diaz Martin +2
Optimal transport (OT) has gained popularity due to its various applications in fields such as machine learning, statistics, and signal processing. However, the balanced mass requi…
Sliced Optimal Partial Transport
Yikun Bai, Berhnard Schmitzer, Mathew Thorpe +1
Optimal transport (OT) has become exceedingly popular in machine learning, data science, and computer vision. The core assumption in the OT problem is the equal total amount of mas…
Wasserstein Task Embedding for Measuring Task Similarities
Xinran Liu, Yikun Bai, Yuzhe Lu +2
Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most curr…
Understanding Entropic Regularization in GANs
Daria Reshetova, Yikun Bai, Xiugang Wu +1
Generative Adversarial Networks are a popular method for learning distributions from data by modeling the target distribution as a function of a known distribution. The function, o…
Information Constrained Optimal Transport: From Talagrand, to Marton, to Cover
Yikun Bai, Xiugang Wu, Ayfer Ozgur
The optimal transport problem studies how to transport one measure to another in the most cost-effective way and has wide range of applications from economics to machine learning.…