Post Hoc Regression Refinement via Pairwise Rankings

• URL: http://arxiv.org/abs/2508.16495v2
• Date: Wed, 01 Oct 2025 12:27:37 GMT
• Title: Post Hoc Regression Refinement via Pairwise Rankings
• Authors: Kevin Tirta Wijaya, Michael Sun, Minghao Guo, Hans-Peter Seidel, Wojciech Matusik, Vahid Babaei,
• Abstract summary: RankRefine is a model-agnostic, plug-and-play method that refines regression with expert knowledge coming from pairwise rankings.<n>In molecular property prediction task, RankRefine achieves up to 10% relative reduction in mean absolute error.
• Score: 37.14333704508996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate prediction of continuous properties is essential to many scientific and engineering tasks. Although deep-learning regressors excel with abundant labels, their accuracy deteriorates in data-scarce regimes. We introduce RankRefine, a model-agnostic, plug-and-play post hoc method that refines regression with expert knowledge coming from pairwise rankings. Given a query item and a small reference set with known properties, RankRefine combines the base regressor's output with a rank-based estimate via inverse variance weighting, requiring no retraining. In molecular property prediction task, RankRefine achieves up to 10% relative reduction in mean absolute error using only 20 pairwise comparisons obtained through a general-purpose large language model (LLM) with no finetuning. As rankings provided by human experts or general-purpose LLMs are sufficient for improving regression across diverse domains, RankRefine offers practicality and broad applicability, especially in low-data settings.

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