aiqbench analysis
This research introduces MixConfig, a novel feature augmentation module that adaptively weights structurally stable clustering configurations for downstream prediction tasks. By jointly reasoning about sample context and cluster stability statistics, the method achieves consistent performance gains across diverse domains like molecular modeling and vision, particularly in low-data regimes where static baselines struggle. The work advances machine learning by replacing ad hoc resolution choices with a dynamic framework that leverages inherent structural variations within data embeddings.
Mixing Configurations for Downstream Prediction
arXiv:2510.19248v2 Announce Type: replace Abstract: 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.
Abstract: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.
16 pages, 5 figures, 10 tables. Accepted at ICML 2026. Equal contribution: Juntang Wang, Hao Wu, Yihan Wang
Machine Learning (cs.LG)
arXiv:2510.19248 [cs.LG]
(or arXiv:2510.19248v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2510.19248 Focus to learn more arXiv-issued DOI via DataCite
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