英文标题：
《Robust Classification of Financial Risk》
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作者：
Suproteem K. Sarkar, Kojin Oshiba, Daniel Giebisch, Yaron Singer
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最新提交年份：
2018
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英文摘要：
Algorithms are increasingly common components of high-impact decision-making, and a growing body of literature on adversarial examples in laboratory settings indicates that standard machine learning models are not robust. This suggests that real-world systems are also susceptible to manipulation or misclassification, which especially poses a challenge to machine learning models used in financial services. We use the loan grade classification problem to explore how machine learning models are sensitive to small changes in user-reported data, using adversarial attacks documented in the literature and an original, domain-specific attack. Our work shows that a robust optimization algorithm can build models for financial services that are resistant to misclassification on perturbations. To the best of our knowledge, this is the first study of adversarial attacks and defenses for deep learning in financial services.
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中文摘要：
算法是高影响力决策中越来越常见的组成部分，越来越多关于实验室环境中对抗性示例的文献表明，标准机器学习模型并不可靠。这表明，现实世界的系统也容易受到操纵或错误分类的影响，这尤其对金融服务中使用的机器学习模型构成了挑战。我们使用贷款等级分类问题来探索机器学习模型如何对用户报告数据的微小变化敏感，使用文献中记录的对抗性攻击和原始的特定领域攻击。我们的工作表明，稳健的优化算法可以为金融服务建立抗误分类干扰的模型。据我们所知，这是针对金融服务领域深入学习的对抗性攻击和防御的首次研究。
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分类信息：

一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Computer Science 计算机科学
二级分类：Cryptography and Security 密码学与安全
分类描述：Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
涵盖密码学和安全的所有领域，包括认证、公钥密码系统、携带证明的代码等。大致包括ACM主题课程D.4.6和E.3中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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