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arXiv Machine Learning By Elaheh Akbari, James Liu, Keaton Hamm, Soheil Kolouri

Sliced Wasserstein Sketches with Error Certificates

Read the original on arXiv Machine Learning →

The paper introduces compact representations called sliced Wasserstein sketches that enable efficient, geometrically meaningful comparisons of probability distributions. It presents an exact decomposition of the squared distance error into residual and partition‑mismatch components, providing computable error certificates for any centroid sketch. Experiments demonstrate that optimal sketches reduce storage while maintaining classification performance, improve generalization with limited labels, and can be used as compact pooling layers in end‑to‑end learning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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