Skip to content
Back to skills

Minibatch Ot Coupling

ASecurity

Compute deterministic squared-cost minibatch optimal transport pairings for OT conditional flow matching recovery runs.

  • 247 stars
  • 0 votes
  • 0 copies
  • 1 view
  • Added September 9, 2026
toolspython

Works with

  • cli

Security analysis

A100/100

Scanned September 9, 2026

npx -y skills add VectorSpaceLab/AREX-Skill --skill minibatch_ot_coupling --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Minibatch Ot Coupling?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Minibatch Ot Coupling
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/vectorspacelab-minibatch-ot-coupling/badge)](https://www.skillsdirectory.com/skills/vectorspacelab-minibatch-ot-coupling)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

SKILL.md
---
name: minibatch_ot_coupling
description: Compute deterministic squared-cost minibatch optimal transport pairings for OT conditional flow matching recovery runs.
---

# Minibatch OT Coupling

Use this skill when implementing OT-CFM or validating whether a recovery experiment pairs source and target samples according to a minibatch optimal transport plan. Do not use the exact enumerator for large minibatches.

## Inputs
- Equal-sized source and target minibatches of numeric vectors.
- Optional comparison pairing, such as an independent or shuffled permutation.

## Outputs
- Minimum-cost permutation mapping each source index to a target index.
- Squared Euclidean transport cost for the assignment.
- Pair records that downstream CFM objective code can consume.

## Workflow
1. Build the squared Euclidean cost matrix.
2. Solve a one-to-one minimum assignment. The bundled script enumerates permutations and is intended for small deterministic tests.
3. Return paired source/target indices and costs.
4. Compare against independent pairings when validating the OT-CFM mechanism.

## Validation
Run `python tests/test_minibatch_ot.py` or the Distiller skill-tree validator with tests enabled.

## Limitations
The bundled solver is factorial in batch size and should be replaced with a Hungarian, Sinkhorn, or POT implementation for real-scale training.

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…