We study model-agnostic copies of machine learning classifiers. We develop
the theory behind the problem of copying, highlighting its differences with
that of learning, and propose a framework to copy the functionality of any
classifier using no prior knowledge of its parameters or training data
distribution. We identify the different sources of loss and provide guidelines
on how best to generate synthetic sets for the copying process. We further
introduce a set of metrics to evaluate copies in practice. We validate our
framework through extensive experiments using data from a series of well-known
problems. We demonstrate the value of copies in use cases where desiderata such
as interpretability, fairness or productivization constrains need to be
addressed. Results show that copies can be exploited to enhance existing
solutions and improve them adding new features and characteristics.

该研究旨在探讨无模型归属的机器学习分类器，通过提出一个框架来复制任何分类器的功能，同时引入一套度量标准来评估实践中生成的副本，以验证该框架的可用性。研究结果表明，副本可以用于增强现有解决方案并添加新功能和特性。

复制机器学习分类器

Copying Machine Learning Classifiers

We address an important problem in sequence-to-sequence (Seq2Seq) learning
referred to as copying, in which certain segments in the input sequence are
selectively replicated in the output sequence. A similar phenomenon is
observable in human language communication. For example, humans tend to repeat
entity names or even long phrases in conversation. The challenge with regard to
copying in Seq2Seq is that new machinery is needed to decide when to perform
the operation. In this paper, we incorporate copying into neural network-based
Seq2Seq learning and propose a new model called CopyNet with encoder-decoder
structure. CopyNet can nicely integrate the regular way of word generation in
the decoder with the new copying mechanism which can choose sub-sequences in
the input sequence and put them at proper places in the output sequence. Our
empirical study on both synthetic data sets and real world data sets
demonstrates the efficacy of CopyNet. For example, CopyNet can outperform
regular RNN-based model with remarkable margins on text summarization tasks.

本研究提出了一种名为 CopyNet 的神经网络模型，集成了新的复制机制，可选择性地将输入序列中的子序列放置在输出序列的适当位置，对于文本摘要等任务表现更佳。