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Found 3 papers, 2 papers with code
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers
Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation.
On the Approximation Properties of Random ReLU Features
We study the approximation properties of random ReLU features through their reproducing kernel Hilbert space (RKHS).
But How Does It Work in Theory? Linear SVM with Random Features
We prove that, under low noise assumptions, the support vector machine with $N\ll m$ random features (RFSVM) can achieve the learning rate faster than $O(1/\sqrt{m})$ on a training set with $m$ samples when an optimized feature map is used.
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