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arXiv Machine Learning By Xinyang Wen

DrainSinkhorn: Safe Elimination for Batched Entropic Optimal Transport

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DrainSinkhorn is a verifier‑gated active‑packing layer that improves batched entropic optimal transport (EOT) by eliminating finished problems from subsequent Sinkhorn updates. It combines candidate‑axis packing, a one‑sided screen, verifier‑gated retirement, and physical compaction, while keeping the EOT objective, per‑instance map, and stopping rule unchanged. The method achieves state‑of‑the‑art execution speedups—up to 4.11× faster on MetroPT‑3 and 3.80× on ImageNet‑32 feature couplings—across multiple backends and tolerance settings. whyItMatters":"The technique delivers significant runtime reductions for heterogeneous batched‑EOT workloads, enabling faster and more efficient optimal transport computations in practical machine‑learning pipelines."

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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