通过自适应混合多种聚类配置，提升小样本下的预测性能。
Mixing Configurations for Downstream Prediction
- 从固定分辨率聚类转向动态混合多种结构稳定配置。
- 在表格、分子、视觉和文本数据上均显著优于单一分辨率基线。
- 特别适合数据稀缺场景，且可无缝集成到现有模型中。
聚类特征在机器学习中广泛应用，但多数方法需预先设定全局、固定且随意的分辨率。最新研究发现，调整分辨率仅产生有限个结构稳定的聚类划分，称为配置。基于此，本文提出配置混合预测（CMP）框架，让模型根据每个样本自适应加权不同配置进行下游预测。我们设计了MixConfig模块，作为即插即用的特征增强组件，能从任意冻结嵌入中提取配置，并通过新型选择器学习能量感知的混合权重，该选择器联合考虑样本上下文、聚类分配与稳定性统计。在表格、分子、视觉和文本等多个领域实验表明，该方法在不同预测器架构下均持续优于单分辨率及静态基线，尤其在低数据环境下表现突出。
原文摘要 · Abstract (English)
Clustering-based features are widely used in machine learning, but most methods must choose a resolution -- a choice that is global, fixed, and ad hoc. Recent work shows that varying the resolution parameter produces only a finite set of structurally stable partitions, known as configurations. Based on this, we introduce Configuration-Mixed Prediction (CMP), a setting where models learn to adaptively weight these configurations per sample for downstream prediction. We propose MixConfig, a plug-and-play feature augmentation module that extracts configurations from any frozen embedding and learns energy-aware mixing weights via a novel selector that jointly reasons about sample context, cluster assignments, and stability statistics. Experiments across tabular, molecular, vision, and text domains demonstrate consistent improvements over single-resolution and static baselines across diverse predictor architectures, with gains particularly pronounced in low-data regimes.
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