为贝叶斯神经网络设计风险敏感认证框架，提升复杂场景下的鲁棒性评估精度。
Risk-Averse Certification of Bayesian Neural Networks
- 用采样与优化结合方法，通过模板多面体近似贝叶斯网络输出集。
- 引入条件风险价值（CVaR）量化最差风险情景下的性能，获得概率保证。
- 在回归与分类任务中表现优于当前最优方法，认证边界更紧、效率更高。
鉴于现实环境固有的复杂性和动态性，引入风险度量对深度学习模型的鲁棒性评估至关重要。本文提出一种面向贝叶斯神经网络的风险敏感认证框架 RAC-BNN。该方法结合采样与优化，以一组模板多面体表示贝叶斯网络的输出集，并引入一致的畸变风险度量——条件风险价值（CVaR），基于采样得到的经验分布提供概率保证。我们在多个回归与分类基准上验证 RAC-BNN，结果表明其能有效量化最差风险情景下的鲁棒性，在复杂任务中实现更紧的认证边界和更高的计算效率，显著优于当前最先进的方法。
原文摘要 · Abstract (English)
In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In this work, we propose a Risk-Averse Certification framework for Bayesian neural networks called RAC-BNN. Our method leverages sampling and optimisation to compute a sound approximation of the output set of a BNN, represented using a set of template polytopes. To enhance robustness evaluation, we integrate a coherent distortion risk measure--Conditional Value at Risk (CVaR)--into the certification framework, providing probabilistic guarantees based on empirical distributions obtained through sampling. We validate RAC-BNN on a range of regression and classification benchmarks and compare its performance with a state-of-the-art method. The results show that RAC-BNN effectively quantifies robustness under worst-performing risky scenarios, and achieves tighter certified bounds and higher efficiency in complex tasks.
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