Learning Unbiased Permutations via Flow Matching
📰 ArXiv cs.AI
Learn to generate unbiased permutations using PermFlow, a conditional flow matching framework, and improve sorting, ranking, and matching tasks
Action Steps
- Implement PermFlow using PyTorch or TensorFlow to learn unbiased permutations
- Apply the closed-form tangent-space projector to preserve unit row and column sums
- Use PermFlow to generate permutations for sorting, ranking, and matching tasks
- Evaluate the performance of PermFlow using metrics such as permutation accuracy and entropy
- Compare PermFlow with existing differentiable methods, such as Sinkhorn, to assess its advantages
Who Needs to Know This
Machine learning researchers and engineers working on ranking, sorting, and matching tasks can benefit from this framework to generate unbiased permutations
Key Insight
💡 PermFlow generates unbiased permutations by operating directly on the affine subspace of matrices with unit row and column sums
Share This
📈 Learn unbiased permutations with PermFlow! 🚀 Improve sorting, ranking, and matching tasks with this conditional flow matching framework 💡
Full Article
Title: Learning Unbiased Permutations via Flow Matching
Abstract:
arXiv:2605.16755v1 Announce Type: cross Abstract: Learning permutations is fundamental to sorting, ranking, and matching, but existing differentiable methods based on entropy-regularized Sinkhorn produce a single softened solution and collapse under ambiguity. We present PermFlow, a conditional flow matching framework that operates directly on the affine subspace of matrices with unit row and column sums. A closed-form tangent-space projector preserves these constraints exactly along every traje
Abstract:
arXiv:2605.16755v1 Announce Type: cross Abstract: Learning permutations is fundamental to sorting, ranking, and matching, but existing differentiable methods based on entropy-regularized Sinkhorn produce a single softened solution and collapse under ambiguity. We present PermFlow, a conditional flow matching framework that operates directly on the affine subspace of matrices with unit row and column sums. A closed-form tangent-space projector preserves these constraints exactly along every traje
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