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Found 6 papers, 5 papers with code
Importance Sparsification for Sinkhorn Algorithm
Sinkhorn algorithm has been used pervasively to approximate the solution to optimal transport (OT) and unbalanced optimal transport (UOT) problems.
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation
Theoretically, we show the selected subsample can be used for efficient density estimation by deriving the convergence rate for the proposed subsample kernel density estimator.
Hilbert Curve Projection Distance for Distribution Comparison
Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling.
Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification
As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs.
Large-scale optimal transport map estimation using projection pursuit
We theoretically show the proposed dimension reduction method can consistently estimate the most ``informative'' projection direction in each iteration.
Sufficient dimension reduction for classification using principal optimal transport direction
The proposed method, named principal optimal transport direction (POTD), estimates the basis of the SDR subspace using the principal directions of the optimal transport coupling between the data respecting different response categories.
