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Convex Distance Operator Transport: A Convex and Geometry-Preserving Formulation

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arXiv:2606. 02047v1 Announce Type: new Abstract: We introduce Convex Distance Operator Transport (CDOT), the first convex optimal transport framework that aligns distributions across heterogeneous domains by jointly preserving feature correspondence and intrinsic geometric structure.

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Authors: Junhyoung Chung, Euijong Song, Won Hwa Kim, Gunwoong Park
Released: 2026-06-02 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Convex Distance Operator Transport: A Convex and Geometry-Preserving Formulation overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)". Matching terms: class, under, source, rate, compare. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.02047

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