AUCPro:以AUC为导向的可证明强度学习
概括
本研究介绍了AUCPro,这是一个在深度神经网络 (DNN) 中可证明稳健性的新框架,它解决了不平衡的数据集. AUCPro针对ROC曲线下的区域 (AUC) 进行了优化,提高了现实世界安全关键应用中的模型可靠性.
科学领域:
- 机器学习 机器学习
- 深度神经网络 深度神经网络
- 竞争力强度 竞争力强度
背景情况:
- 对于深度神经网络 (DNN) 的传统可证明稳定性方法通常假设平衡的类分布.
- 现实世界的应用程序,特别是安全敏感的系统,经常遇到不平衡的数据集,长尾分布.
- 与准确性相比,ROC曲线下的面积 (AUC) 是评估不平衡数据集上的模型性能的一个更合适的指标.
研究的目的:
- 为长尾分布设计的第一个以AUC为导向的可证明稳定性学习框架 (AUCPro).
- 从理论上分析拟议的AUCPro框架的认证安全区域和稳定性概括.
- 在基于AUC的稳健性背景下,研究性能-稳健性权衡和过度风险.
主要方法:
- 开发了AUCPro,这是一个利用由同位素高斯噪声平滑的代理模型的框架.
- 从面向AUC的学习角度优化了代理模型.
- 获得了针对 $\ell _{2}$l2 对抗性攻击的认证安全区域的理论保证,并提出了强度概括的新型标准.
主要成果:
- 为AUCPro建立了一个经过认证的安全区域,确保该区域内不受敌对攻击.
- 提出了一个新的理论框架来研究可证明的稳定性方法的稳定性概括,将其与AUC的对抗性风险联系起来.
- 通过全面的实验证明了AUCPro的有效性,验证了其性能和通用化能力.
结论:
- 对于面临数据分布不平衡的深度神经网络,AUCPro提供了一个强大的解决方案.
- 该研究引入了一种新的方法,用于理解和改进可证明的强度学习中的强度概括.
- 这些发现对于在安全关键领域开发可靠的人工智能系统至关重要,这些领域具有长尾数据.
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