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Xun Tang

15 papers hereh-index 579 citations22 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author13
  • middle author2

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • math.NA9
  • math.OC4
  • math.PR1
  • quant-ph1
same name
  • Xun Tang — 3 papers, h 13
  • Xun Tang — 2 papers
  • Xun Tang — 2 papers, h 3
  • Xun Tang — 2 papers, h 3
  • Xun Tang — 1 paper, h 2
  • Xun Tang — 1 paper, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedA Sinkhorn-type Algorithm for Constrained Optimal Transport

1 citations · 1 across the 11 of their papers we have counts for

collaborators
Showing math.OCShow all

4 papers · 1 filter

math.OC2026

Numerically stable variants of overrelaxation for operator Sinkhorn iteration

Henrik Eisenmann, Tasuku Soma, Xun Tang +1

We consider accelerated versions of the operator Sinkhorn iteration (OSI) for solving scaling problems for completely positive maps. Based on the interpretation of OSI as alternati…

math.OC2025

An efficient algorithm for entropic optimal transport under martingale-type constraints

Xun Tang, Michael Shavlovsky, Holakou Rahmanian +2

This work introduces novel computational methods for entropic optimal transport (OT) problems under martingale-type conditions. The considered problems include the discrete marting…

math.OC2024★ 1 cited

A Sinkhorn-type Algorithm for Constrained Optimal Transport

Xun Tang, Holakou Rahmanian, Michael Shavlovsky +3

Entropic optimal transport (OT) and the Sinkhorn algorithm have made it practical for machine learning practitioners to perform the fundamental task of calculating transport distan…

math.OC2024

Accelerating Sinkhorn Algorithm with Sparse Newton Iterations

Xun Tang, Michael Shavlovsky, Holakou Rahmanian +4

Computing the optimal transport distance between statistical distributions is a fundamental task in machine learning. One remarkable recent advancement is entropic regularization a…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.