使用受信任项的训练集验证
Training Set Debugging Using Trusted Items
摘要：
训练集错误是数据中的缺陷，会对机器学习产生不利影响。训练集对于人工检查来说通常太大，但是可以有资源来验证一些可信项目。可信项的集合本身可能不足以用于学习，因此我们提出了一种算法，该算法使用这些项来识别训练集中的错误，从而改进学习。具体来说，我们的方法寻求训练集标签的最小变化集，以便从这个校正的训练集学习的模型正确地预测可信项目的标签。我们将标签被更改的项目标记为潜在的bug，其标签可以由人类专家检查其准确性。以这种方式发现错误是一个具有挑战性的组合双层优化问题，但它可以放松为连续优化问题。在玩具和真实数据上的实验表明，该方法可以有效地识别训练集错误，并对标签提出适当的修改。我们的算法是迈向可信的机器学习的一步。
英文摘要：
Training set bugs are flaws in the data that adversely affect machine learning. The training set is usually too large for man- ual inspection, but one may have the resources to verify a few trusted items. The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these items to identify bugs in the training set and thus im- proves learning. Specifically, our approach seeks the smallest set of changes to the training set labels such that the model learned from this corrected training set predicts labels of the trusted items correctly. We flag the items whose labels are changed as potential bugs, whose labels can be checked for veracity by human experts. To find the bugs in this way is a challenging combinatorial bilevel optimization problem, but it can be relaxed into a continuous optimization problem. Ex- periments on toy and real data demonstrate that our approach can identify training set bugs effectively and suggest appro- priate changes to the labels. Our algorithm is a step toward trustworthy machine learning.
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。
