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A Sinkhorn-type Algorithm for Constrained Optimal Transport
We derive the corresponding entropy regularization formulation and introduce a Sinkhorn-type algorithm for such constrained OT problems supported by theoretical guarantees.
Accelerating Sinkhorn Algorithm with Sparse Newton Iterations
To achieve possibly super-exponential convergence, we present Sinkhorn-Newton-Sparse (SNS), an extension to the Sinkhorn algorithm, by introducing early stopping for the matrix scaling steps and a second stage featuring a Newton-type subroutine.
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
On tabular data, a significant body of literature has shown that current deep learning (DL) models perform at best similarly to Gradient Boosted Decision Trees (GBDTs), while significantly underperforming them on outlier data.
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