论文标题
通过学习优化来解释AI
Explainable AI via Learning to Optimize
论文作者
论文摘要
难以理解的黑匣子在机器学习（ML）中很常见，但是应用程序越来越需要可解释的人工智能（XAI）。 XAI的核心是建立透明且可解释的数据驱动算法。这项工作为XAI提供了具体的工具，在必须对先验知识进行编码和不信任的推论的情况下。我们使用“学会优化”（L2O）方法，其中每个推理都解决了数据驱动的优化问题。我们的L2O模型直接实施，直接编码先验知识并产生理论保证（例如，约束满意度）。我们还建议使用可解释的证书来验证模型推断是否值得信赖。基于字典的信号恢复，CT成像和加密集结的套利交易的应用中提供了数值示例。可以在https://xai-l2o.research.typal.academy上找到代码和其他文档。
Indecipherable black boxes are common in machine learning (ML), but applications increasingly require explainable artificial intelligence (XAI). The core of XAI is to establish transparent and interpretable data-driven algorithms. This work provides concrete tools for XAI in situations where prior knowledge must be encoded and untrustworthy inferences flagged. We use the "learn to optimize" (L2O) methodology wherein each inference solves a data-driven optimization problem. Our L2O models are straightforward to implement, directly encode prior knowledge, and yield theoretical guarantees (e.g. satisfaction of constraints). We also propose use of interpretable certificates to verify whether model inferences are trustworthy. Numerical examples are provided in the applications of dictionary-based signal recovery, CT imaging, and arbitrage trading of cryptoassets. Code and additional documentation can be found at https://xai-l2o.research.typal.academy.
