---
title: "Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.41166v1"
permalink: "https://feed.astralor.com/items/itm_d6e0be5587d9b825ea3cfdcb"
published: "2026-10-30T02:42:41.000Z"
score: 53
channel: "tech"
tags: ["RAG","Adaptive Uncertainty Quantification","Simplicial Complexes","Conformalized Simplicial Neural","Networks Simplicial","SNNs","SCs","However"]
---

# Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks

## 速览

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 rig…

## 关键点

- 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.

## 全文

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.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_d6e0be5587d9b825ea3cfdcb