相似性学习者模型不可知解释的类比和特征归因
Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners
摘要：
黑箱模型的事后解释在分类和回归设置中得到了广泛的研究。然而，对两个输入之间输出相似性的模型的解释受到的关注相对较少。在本文中，我们提供了适用于表格和文本数据的相似性学习模型不可知的本地解释。我们首先提出了一种方法，提供特征属性来解释由黑盒相似性学习器确定的一对输入之间的相似性。然后，我们提出类比作为机器学习中一种新的解释形式。在这里，目标是识别与输入对具有相同相似性的不同类似示例对，并提供对模型预测潜在（潜在）因素的洞察。类比的选择可以选择性地利用特征属性，从而连接两种形式的解释，同时仍然保持互补性。我们证明了我们的类比目标函数是次模块化的，使得搜索高质量的类比有效。我们应用所提出的方法来解释句子之间的相似性预测的一个国家的最先进的句子编码器，和患者之间的医疗保健利用应用。通过定量评估、仔细的用户研究和解释示例来衡量功效。
英文摘要：
Post-hoc explanations for black box models have been studied extensively in classification and regression settings. However, explanations for models that output similarity between two inputs have received comparatively lesser attention. In this paper, we provide model agnostic local explanations for similarity learners applicable to tabular and text data. We first propose a method that provides feature attributions to explain the similarity between a pair of inputs as determined by a black box similarity learner. We then propose analogies as a new form of explanation in machine learning. Here the goal is to identify diverse analogous pairs of examples that share the same level of similarity as the input pair and provide insight into (latent) factors underlying the model's prediction. The selection of analogies can optionally leverage feature attributions, thus connecting the two forms of explanation while still maintaining complementarity. We prove that our analogy objective function is submodular, making the search for good-quality analogies efficient. We apply the proposed approaches to explain similarities between sentences as predicted by a state-of-the-art sentence encoder, and between patients in a healthcare utilization application. Efficacy is measured through quantitative evaluations, a careful user study, and examples of explanations.
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。
