Query 2.0的训练驱动训练数据训练
Complaint-driven Training Data Debugging for Query 2.0
摘要：
随着对机器学习（ML）的需求在所有行业领域迅速增长，商业数据库提供商对支持“查询2.0”（将模型推理集成到SQL查询中）有着浓厚的兴趣。由于训练数据中的错误可能导致意外的查询结果（例如，错误的标签，损坏的特征）。作为回应，我们提出了雨，一个投诉驱动的训练数据调试系统。Rain允许用户指定对查询的中间或最终输出的抱怨，并旨在返回一组最小的训练样本，以便如果删除它们，抱怨将得到解决。据我们所知，我们是最早研究这个问题的。一个简单的解决方案需要重新训练指数数量的ML模型。我们提出了两种新的启发式方法的基础上的影响函数，都需要线性再训练步骤。我们对这两种方法进行了深入的分析和实证分析，并使用四个真实世界的数据集进行了广泛的实验，以评估其有效性。结果表明，Rain在所有基线中实现了最高的召回率@k，同时仍然以交互方式返回结果。
英文摘要：
As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2.0", which integrates model inference into SQL queries. Debugging Query 2.0 is very challenging since an unexpected query result may be caused by the bugs in training data (e.g., wrong labels, corrupted features). In response, we propose Rain, a complaint-driven training data debugging system. Rain allows users to specify complaints over the query's intermediate or final output, and aims to return a minimum set of training examples so that if they were removed, the complaints would be resolved. To the best of our knowledge, we are the first to study this problem. A naive solution requires retraining an exponential number of ML models. We propose two novel heuristic approaches based on influence functions which both require linear retraining steps. We provide an in-depth analytical and empirical analysis of the two approaches and conduct extensive experiments to evaluate their effectiveness using four real-world datasets. Results show that Rain achieves the highest recall@k among all the baselines while still returns results interactively.
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。
