CF-SNN：为单纯形神经网络提供覆盖率有保证的不确定性量化
Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks
做高阶图数据预测的朋友可以看看：CF-SNN 给单纯形神经网络加上带覆盖率保证的不确定性量化，多目标也能同时控住误覆盖率。
单纯形神经网络（SNN）用于处理存储在单纯复形（SC）上的高阶关系数据，但一直缺乏严格的不确定性量化（UQ）手段，在高风险场景中难以放心使用。论文提出 conformalized SNN（CF-SNN），对含 m 个预测目标的实体同时生成 m 个预测集。在温和假设下，CF-SNN 保证每个预测集以用户指定的名义覆盖率包含真实标签，且所有目标上的实际误覆盖比例以高概率接近名义水平。大量 SC 预测任务实验显示，CF-SNN 在达成指定覆盖率的同时，预测集规模较小、信息量较高。
Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks
Simplicial neural networks (SNNs) are powerful tools for prediction tasks on higher-order relational datasets stored on simplicial complexes (SCs), a higher-order generalization of graphs. However, SNNs usually lack rigorous uncertainty quantification (UQ), limiting their usefulness in high-stakes scenarios. We propose conformalized SNN (CF-SNN), a framework that, given a simplicial-complex entity with m prediction targets, generates prediction sets for all m targets simultaneously. Under mild assumptions, CF-SNN guarantees that, for each target, the prediction set contains the true label with a user-specified nominal coverage probability, and the realized miscoverage proportion across all m targets is guaranteed to remain close to the nominal level with high probability. Extensive experiments across a wide range of SC prediction tasks demonstrate that CF-SNN is a flexible UQ framework that achieves user-specified coverage while producing informative prediction sets of small size.