3 citations · 3 across the 7 of their papers we have counts for
7 papers
Understanding Learning with Sliced-Wasserstein Requires Rethinking Informative Slices
Huy Tran, Yikun Bai, Ashkan Shahbazi +2
The practical applications of Wasserstein distances (WDs) are constrained by their sample and computational complexities. Sliced-Wasserstein distances (SWDs) provide a workaround b…
Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data
Xinran Liu, Yikun Bai, Rocío Díaz Martín +5
Efficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such…
Sinkhorn algorithms and linear programming solvers for optimal partial transport problems
Yikun Bai
In this note, we generalize the classical optimal partial transport (OPT) problem by modifying the mass destruction/creation term to function-based terms, introducing what we term…
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…
Partial Transport for Point-Cloud Registration
Yikun Bai, Huy Tran, Steven B. Damelin +1
Point cloud registration plays a crucial role in various fields, including robotics, computer graphics, and medical imaging. This process involves determining spatial relationships…
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…