信任但验证：基于反事实约束学习的预测可信度研究
Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
摘要：
以置信区间或概率分布的形式表示的预测可信度度量是统计学和机器学习的基础，用于表征模型的鲁棒性，检测分布外的样本（离群值），并防止对抗攻击。为了有效，这些措施应该（i）考虑到实践中使用的各种模型，（ii）对于训练模型是可计算的，或者至少避免修改已建立的训练程序，（iii）放弃使用数据，这可能会使它们面临与基础模型相同的鲁棒性问题和攻击，以及（iv）遵循理论保证。这些原则是本工作中开发的框架的基础，该框架将可信度表达为风险适合权衡，即，在通过扰动模型输入可以改进多少拟合和该扰动的幅度（风险）之间的折衷。使用约束优化配方和对偶理论，我们分析了这种妥协，并表明，这种平衡可以确定反事实，而不必测试多个扰动。这导致了一种无监督的后验方法，该方法为任何（可能非凸）可微模型分配预测可信度，从基于RKHS的解决方案到任何（前馈，卷积，图形）神经网络架构。它的使用说明在数据过滤和防御对抗攻击。
英文摘要：
Prediction credibility measures, in the form of confidence intervals or probability distributions, are fundamental in statistics and machine learning to characterize model robustness, detect out-of-distribution samples (outliers), and protect against adversarial attacks. To be effective, these measures should (i) account for the wide variety of models used in practice, (ii) be computable for trained models or at least avoid modifying established training procedures, (iii) forgo the use of data, which can expose them to the same robustness issues and attacks as the underlying model, and (iv) be followed by theoretical guarantees. These principles underly the framework developed in this work, which expresses the credibility as a risk-fit trade-off, i.e., a compromise between how much can fit be improved by perturbing the model input and the magnitude of this perturbation (risk). Using a constrained optimization formulation and duality theory, we analyze this compromise and show that this balance can be determined counterfactually, without having to test multiple perturbations. This results in an unsupervised, a posteriori method of assigning prediction credibility for any (possibly non-convex) differentiable model, from RKHS-based solutions to any architecture of (feedforward, convolutional, graph) neural network. Its use is illustrated in data filtering and defense against adversarial attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
